Skoltech is an international graduate research-focused university that was founded by the group of world-renowned scientists in 2011. Skoltech's curriculum focuses on technology and innovation, offering Master's programs in 11 technological disciplines. Students receive rigorous theoretical and practical training, design their own research projects, participate in internships and gain entrepreneurial skills in English. The faculty is comprised of current researchers with international accreditation and achievements.
Course Catalog for 2023-2024
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Term 1
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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3D CAD Modeling and Design
3D modeling is an integral part of a modern design. This course will help students to learn basics of CAD modeling, approaches of design and engineering of parts and assemblies needed to work in various fields of engineering and Computer-Generated Imagery (CGI). This is applied course relies in design development through CAD software, which allows engineers and designers to quickly implement technical ideas, conduct experiments, perform calculations, create electronic product models and detailed drawings.
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Artem Aleksanyan | 3 | MA030526 | ||
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Advanced Drilling and Completion Technologies (Term 1B)
The course will cover planning and execution phases of basic and advanced drilling and completion technologies. The course will also cover basics of the offshore drilling and completion.
The planning phase includes basics of well design: selection of trajectory, casing design, drilling fluid selection, drilling string design, bottom hole assembly (drilling bits, rotary steerable systems, mud motors, measured while drilling and logging while drilling tools) selection, cementing design, lower completion and upper completion design, wellheads and x-mass tree selection, drilling rig selection, hydraulic fracturing design for vertical and horizontal wells, etc. Execution phase covers techniques of directional drilling, hole cleaning, casing running, cementing operation, completion running, pumping fracturing jobs, emergency situations prevention and recovery (well control, stuck pipe, etc.). The course will be useful for MSc and PhD students of Skolkovo Institute of Science and Technology which consider career in the field of oil and gas recovery, or wish to get understanding of oil&gas well construction processes. |
Kirill Bogachev | 3 | MA030347 | CANCELLED | |
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Advanced Quantum Mechanics (Term 1B)
Lecture Course “Advanced Quantum Mechanics” comprises a number of topics which are not included in standard courses on Quantum Mechanics. Meanwhile, these topics acquire increasing importance during last 2-3 decades due to developing applications in various branches of quantum condensed-matter physics theory. The first set of topics refers to examples of adiabatic or weakly non-adiabatic behavior of quantum system: Berry phases and Landau-Zener tunneling. Secondly, we discuss Feynman path integral representation of quantum mechanics. The third part of the course is devoted to the density matrix formalism for description of open systems.
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Aleksandr Korotkevich | 3 | DA030207 |
MOVEDmoved to Term 3
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Advanced Reservoir Modeling and Engineering
This course is is covered base and advanced theory and practice of reservoir engineering aimed to merge development, technological and modeling parts that leads to digital oilfield. In addition, main part of this course is practical hydrodynamics modeling of reservoir development:
1) Modern physics of oil and gas displacement/hydrodynamics, stages of reservoir development, well drilling and problems during reservoir development. 2) Russian and international ways of oil and gas reserves calculation, required technical documentation of reservoir development plan. 3) Advanced of hydrodynamic modeling, including theory of hydrodynamic, practical course of modern hydrodynamic simulators (CMG Stars/GEM/IMEX – for EOR modeling, including thermal and chemical methods, RFD tNavigator – as modern Russian simulator, which allow to fast solving of practical tasks) 4) Expertise of hydrodynamic models (input information, quality of model building, quality of matching and forecast results). 5) Complications during lift of oil and gas through production wells and pipeline systems and methods of prevention. 6) Integrated modeling (reservoir-wells-surface): Introduction, initial data 7) Integrated modeling: How to created model. 8) Integrated modeling: Modeling in Petex and tNavigator |
Alexander Cheremisin | 6 | MA060540 | ||
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Advanced Solvers for Numerical PDEs
Partial differential equations (PDEs) describe many phenomena around us: from tsunami propagation to neural currents in the human head. Given today’s accuracy demands in industrial and academic applications, numerical solution of the PDEs typically involves millions and even billions of unknowns. This course presents and analyses efficient algorithms for their fast and economical solution.
We discuss established approaches based on iterative solvers (Krylov subspace solvers) and robust economical preconditioners (FFT-related, geometric and algebraic multigrid, and others) for the arising large discrete problems. We also look at the possibilities of machine learning and dimensionality reduction methods for numerical solution of the PDEs. We will concentrate on diffusive, time-harmonic electromagnetic and acoustic systems and models of fluid dynamics. Special emphasis will be on the design of the solvers for anisotropic and/or highly-heterogeneous problems, problems on nonuniform and unstructured computational grids. Numerical modeling with both standard (finite-difference) and advanced discretization methods will be considered (finite volume, finite element, mixed finite element, and discontinuous Galerkin). This course is intended to attract student of all majors interested |
Nikolay Yavich | 3 | MA030470 | ||
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Bayesian Methods of Machine Learning (Term 1B-2)
The course addresses Bayesian approach for solving various machine learning and data analysis problems. It offers the framework for solving inverse problems, model selection and continual learning. The most attention is payed to the fusion between the deep learning techniques and the elements of bayesian approach. This fusion is possible due to the development of a range of approximate inference techniques in the last 20 years.
Modern machine learning papers frequently use machinery of the approximate inference techniques. The main learning outcome of the course is the ability to read and reproduce related papers, and to apply corresponding methods of approximate inference for the development of Bayesian machine learning approaches. In order to reach this goal, the course contains theoretical and practical assignments and the final project. The purpose of the theoretical tasks is two-fold. Firstly, we would like to develop skills of equation derivation, that is used routinely in papers and is usually suppressed. Secondly, some theoretical tasks provide the intuition about properties of the methods through toy models. The purpose of the practical tasks is straightforward: to translate discussed methods to the code and observe properties of the methods through examples. These assignments leads to the course projects: reproducing and discussing a relevant paper from a recent conference, and/or development of some students' research ideas using methods of Bayesian machine learning. |
Evgeny Burnaev |
63 per term
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MA060129 | ||
| Cluster Varieties and Integrable Systems (Term 1-2) | Andrey Marshakov |
63 per term
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MA060597 | ||
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Communication Technologies for IoT (Term 1B)
The course "Communication Technologies for IoT" prepares students for the applying modern telecommunication technologies, both wired and wireless, in the Internet of Things area. The course combines lectures and labs related to hardware, transmission techniques, the medium-access control layer, networking, applications and standards for the IoT communication technologies. All technologies are considered with use-case based approach that shows their practical application in real industrial and research scenarios.
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Dmitry Lakontsev | 3 | MA030234 |
MOVEDmoved to AY 2024-2025
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Computational Fluid Dynamics
Fluid flows are ubiquitous in engineering. Fluid mechanics provides the theoretical foundation to a broad spectrum of engineering applications that range from tiny laboratory-on-a-chip devices to the largest thermal and hydroelectric power plants. The rich dynamics of fluid motion leads to numerous effects that engineers may wish to exploit or suppress. Mathematical description of fluid flows most commonly involves non-linear partial differential equations that make analytical solution impossible or impractical. Therefore, approximate solution using numerical methods has been widely implemented since the advent of digital computers. Nowadays, the Computational Fluid Dynamics (CFD) is a well-established discipline. CFD is widely used in science and engineering alike. It accelerates optimal design and offers important insights in the flow dynamics.
The course will introduce the students to important theoretical and practical aspects of the CFD. It will explain how to describe the fluid flow by partial differential equations with suitable initial and boundary conditions, and how to transform those equations into computer algorithms. A brief overview of general-purpose numerical methods will be provided, with comments on their relevance to the CFD. Then, specialized methods for different types of flows will be introduced. This will be followed by a brief discussion of the advanced topics of turbulence modelling, fluid-structure interaction, multiphase flows. Computer practice classes will allow the students to acquire basic skills of programming simple methods from scratch, get acquainted with existing CFD software packages, develop intuition for distinguishing physical effects from numerical artifacts, and learn to use the CFD wisely. |
Dmitry Kolomenskiy | 6 | MA060450 | ||
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Computational Imaging (Term 1B)
In the computational era of everything, imaging has not become an exception. Computational algorithms allow both to extract valuable information from a scene and to improve the very sensor that forms the image. Today, computational and image processing enhancements became integrable parts of any digital imager, be it a miniature smartphone camera or a complex space telescope.
This crash course is designed as a prerequisite for those students who would like to venture into the field of Computer Vision. We will cover foundational mathematical equations that are involved in the image formation and in the geometric projection principles. The concept of Point Spread Function that distorts the object will be explained on particular examples and will be experimented with for the tasks of image reconstruction and denoising. Image processing will be covered with an emphasis on the Python libraries to be used in the rest of the imaging-related courses on the DS/IST tracks (openCV and others). A basic DSLR photo camera will be considered as a model for understanding Fourier Imaging and Filtering methods in a laboratory exercise. Hands-on tutorials on how to select a camera and a lens for your machine vision application will be provided. The theory of color and stereo light-field cameras will be covered using the models of commonplace Bayern RGB sensors; as well as state-of-art spectral and multi-lens imagers. The course will consist of three theoretical lectures riffled by three graded in-class laboratory coding sessions on the subjects covered in the theoretical lectures. 100% attendance is mandatory. There will be a single in-class exam during the evaluation week and no homework. |
Dmitry Dylov | 3 | MA030121 | ||
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Computational Materials Science Seminar (Term 1B-2)
This is the main research seminar at Skoltech for Computational Materials scientists. All students of Computational Materials Science subtrack of Materials Science MSc program and Materials Science and Engineering PhD program should attend this seminar. Topics include materials modeling (at atomistic scale), theoretical and computational chemistry, theoretical and computational physics of materials, underlying mathematical methods and algorithms etc. Invited lectures are top scientists in their research field. Students are welcome to present their own research results at this seminar and expected to do this at least once per two terms.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/ |
Dmitry Aksenov |
30.5 per term
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MA030430 | ||
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Differential Topology (Term 1-2)
The course will cover two topics, which are central in topology of smooth manifolds, the de Rham cohomology theory and Morse theory. The course will culminate in two famous results of differential topology: Smale's h-cobordism theorem and Milnor's discovery of exotic smooth structures on the 7-dimensional sphere. The h-cobordism theorem proved by S. Smale in 1962 is the main (and almost the only) tool for proving that two smooth manifolds (of dimension greater than or equal to 5) are diffeomorphic. In particular, this theorem implies the high-dimensional Poincare conjecture (for manifolds of dimensions 5 and higher). Milnor's discovery of exotic smooth structures on the 7-dimensional sphere and further results of Kervaire and Milnor were the first steps towards surgery theory, which is the most powerful tool for classifying smooth manifolds.
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Alexander Gaifullin |
63 per term
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MA060599 | ||
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Digital Core Processing
The course provides basic theoretical and practical knowledge in reservoir characterization using Digital Rock Physics (DRP) Technologies. DRP technologies are a popular and rapidly growing scientific field of petroleum engineering, which allows not only to predict the main petrophysical properties of porous media, but also to evaluate recoverable hydrocarbon reserves and propose optimal methods for field development. Nowadays analysis for capillary pressure curves, two phase filtration, relative phase permeabilities, displacement coefficients and choosing best scenarios for improved oil recovery (IOR) as well as enhanced oil recovery (EOR) using digital model of rock material and fluids are widely used in industry.
During the course, the students will learn the key DRP approaches, including: • The acquisition on the structure of rocks and voids, minerals volume distribution by X-Ray tomography, electron microscopy, focused ion beam milling for gathering data and building up a digital model of the rock on a macro-, micro- and submicron levels; The technology is applicable, among other things, to solving many problems in the exploration and production of tight and hard-to-recover hydrocarbons reserves. Particular emphasis is made on the validation of the simulations, comparison of simulated vs. experimental data and model tuning as key elements of the correct digital core modelling. The course consists of lectures and seminars, including demonstration of laboratory equipment, used for building up a digital model of the rock. |
Denis Orlov | 3 | MA030563 | ||
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Emerging Technologies for Next Generation Wireless Communications
Fifth generation mobile communications, rapidly deploying all around the globe, are promised to outperform the available solutions in terms of latency, energy-efficiency, and data rates. However, with its peak speed of about 10 Gb/s and channel bandwidth of 0.1-1 GHz this technology will turn out to be inadequate to meet the explosive growth of machine connectivity in the short run. This course is designed to provide the students with basic knowledge of terahertz technology, edge AI hardware design, and reconfigurable intelligent surfaces which are customarily identified as enabling technology towards wireless mobile communications beyond 5G. We will specifically touch upon reconfigurable intelligent surfaces that can potentially lead to enhanced energy and spectrum efficiency of wireless communications. In particular, we will elaborate on space-time-coding digital metasurfaces that are constituted by a programmable array of artificial unit cells each of which is characterized by its own electromagnetic response.
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Dmitry Yudin | 3 | MA030473 | CANCELLED | |
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Engineering Design Factory - Part I (Term 5-6)
This course seeks to emulate real world product development process. It is a project based hands-on course and it is aiming to allow students to develop a concrete product for the market; they will learn and experience a detailed product development cycle including
its modelling, business case, industrial design, prototyping and testing in both the virtual and physical realities. The course comprises a series of lectures on product development methodologies and on the integration of the various dimensions to successfully achieve a product or system demonstrator with a credible market value. Products are assumed to consist of electrical, mechanical and data analysis components. Teams of 4 or 5 students with various backgrounds will be expected to work with industrial clients or bring their own product proposals to develop a valuable product or system starting from a Technology Readiness Level (TRL) of 2 or 3, and aiming to reach a TRL level of 4 or 5. The teams will present their product to external experts and stakeholders; the best products will be promoted by Skoltech to seek external funding. |
Clement Fortin |
63 per term
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MA060603 | ||
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English Toolkit (Term 1B)
The goal of the English Toolkit course is to activate Academic English skills required for successful education at Skoltech.
The students will practice Academic vocabulary and grammar, as well as boost their reading, writing, listening and speaking skills within a range of research-related topics. The chosen format provides the students with a flexible and individualized learning trajectory. Real-time feedback for online exercises is complimented by tutor feedback for the writing and speaking assignments for a better understanding of the main language difficulties, providing an opportunity to improve and see progress. By the end of the course, the students will |
Elizaveta Tikhomirova | 3 | ME030568 | ||
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English. Candidate Examination
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research structured according to the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
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Essential Engineering Toolbox (Term 1B)
The course is a seriеs of tutorials on essential tools that are extensively used throughout coursework and research in the Advanced Manufacturing Technologies program and generally in engineering practice. The tutorials involve hands-on exercises on a computer.
The course introduces Latex, Python, Mathematica, and Matlab that are routinely used during research work. Each topic will consist of an introductory lecture followed by practical exercises. Students will be required to solve particular problems and write reports in Latex using the tools learned in the tutorials. |
Petr Zhilyaev | 3 | MA030351 | ||
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Experimental Data Processing (Term 1B)
The course introduces students to practically useful approaches of data processing for control and forecasting. The focus will be on identifying the hidden and implicit features and regularities of dynamical processes using experimental data. The course exposes data processing methods from multiple vantage points: standard data processing methods and their hidden capacity to solve difficult problems; statistical methods based on state-space models; methods of extracting the regularities of a process on the basis of identifying key parameters. The course addresses the problems in navigation, solar physics, geomagnetism, space weather and biomedical research and will be useful for a broad range of interdisciplinary applications.
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Tatiana Podladchikova | 6 | MA060238 | ||
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Foundations of Software Engineering (Term 1B)
This course is intended to serve as an introduction into basics of everyday industrial software engineering. Oftentimes students seek to obtain proficiency in complicated subjects such as machine learning, algorithms, or computer vision, but lack basic literacy in software engineering and therefore have little practical skills required to carry out research or industrial projects. The course focuses on Unix fundamentals (shell and command line, tools such as vim and awk, scripting, filesystem, streams and pipes, parameter parsing, remote machine and ssh, etc.) and software engineering in teams (code review and version control, building and auto-making programs, reproducibility and containers, testing and test-driven development, improving code style, software deployment and APIs, etc.).
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Aleksandr Mikhalev | 3 | MA030406 | ||
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Fundamentals of Remote Sensing
This course introduces students to the first principles and methods of the observation of Earth surface, monitoring of Earth atmosphere and detection of different kind of radiation coming from Space. The course will cover wide range of the satellites-, aircraft-, rockets- and balloon- based techniques designed for environmental monitoring, meteorology, map making etc. Goals of the course include: a comprehensive knowledge of the principles and approaches to the creation and operation of remote sensing systems; acquisition of analysis skills of modern ERS programs; practical application of acquired knowledge and
skills for SWOT-analysis of complex information systems. Course will also include a module on geomatics, i.e. platforms, sensors and methodologies related to the collection, processing, analysis and interpretation of (2D/3D) data related to Earth's surface. This includes platforms like satellite or drones, sensors like LiDAR or airborne cameras and techniques like photogrammetry, laser scanning, geodesy, topography, etc. Major learning outcomes include operational principles and design of different sensors used in remote sensing of the Earth, practical skills to design an experiment in remote sensing with applications to a practical business need. |
Tatiana Podladchikova | 6 | MA060186 |
MOVEDmoved to Term 3
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Geometric Representation Theory (Term 1-2)
Geometric representation theory applies algebraic geometry to the problems of
representation theory. Some of the most famous problems of representation theory were solved on this way during the last 40 years. The list includes the Langlands reciprocity for the general linear groups over the functional fields, the Langlands-Shelstad fundamental Lemma, the proof of the Kazhdan-Lusztig conjectures; the computation of the characters of the finite groups of Lie type. We will study representations of the affine Hecke algebras using the geometry of affine Grassmannians (Satake isomorphism) and Steinberg varieties of triples (Deligne-Langlands conjecture). This is a course for master students knowing the basics of algebraic geometry, sheaf theory, homology and K-theory. |
Mikhail Finkelberg |
63 per term
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DA060271 | ||
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Innovation Workshop
Innovation Workshop (IW) is a one-month full-time MS-level course that unites the entire Skoltech incoming MS class with Skoltech faculty and invited mentors to create the foundational experience in Entrepreneurship and Innovation (E&I) for all. IW is designed to instill a positive “can-do” attitude in the Skoltech culture, as well as to cultivate the art of prototyping quickly, under pressure, with help from others, and based on whatever resources are at hand here and now. The core IW workflow is the iterative progression through (i) the tangible prototypes of (ii) the technological solution of (iii) the practical problem, that are produced based on (iv) the actual live end user feedback.
. Experiential inquiry-based learning leads IW students through the entire technology innovation cycle along the three pillars of innovation: (i) Impact (Problem + Feedback), (ii) Novelty of the solution (Science + Prototype), and (iii) Vision for the subsequent iterations (Next Steps + Picture of Success). This work is performed in cross-disciplinary teams operating under time pressure thus creating real life experience of complex innovation project. . IW is not a business class competition and not a hard skill class. It is less about knowledge and more about developing skills and attitudes necessary to lead successful life in innovation. It is also an opportunity for students to learn more about Skoltech’s basic values and meet the entire class and most of the faculty in an intensive relationship-building setting. . This file is the abbreviated version of the IW syllabus that carries only the most technical summary information. Please find the full IW Syllabus in the Files section of your IW Canvas page, as well as the attachment to this submission (the clickable "Upload" URL in the bottom of this document). Students of the IW are strongly recommended to read the full Syllabus as it carries plenty of information necessary to succeed in the IW and in innovation in general. . |
Dmitry Kulish | 6 | E&I | MC060001 | |
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Intellectual Property in Industry. Patents, Patent Search and Drafting
This course focuses on the basics of patent search. This process is an integral part of a significant number of R&D projects. Patent search is also the most important type of engineering activity that accompanies any process of new inventions development and application.
In this course, students will be generally acquainted with the concepts and basics of intellectual property protecting, including industrial property. Also as part of this course, students will master necessary knowledge and skills in the field of patent search. The course will be useful to a wide range of specialists who develop new technical solutions and introduce them into production. |
Vadim Sulimov | 3 | MA030528 | CANCELLED | |
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Introduction to Advanced Manufacturing Technologies (Term 1B)
The course provides an introduction to the field of Advanced Manufacturing Technologies and focuses on main research and educational thrusts of the Center for Materials Technologies (https://sci.skoltech.ru/materials): Advanced Manufacturing Technologies, Science of Advanced Manufacturing, and Computational Engineering.
The first thrust is focused on advanced technologies such as Advanced Manufacturing of Composite Materials, Additive Technologies, Thermal Spray Coatings, and Materials Selection in Design. The second thrust is focused on digital engineering technologies related to simulation-driven product development, model based systems engineering, digital manufacturing, product lifecycle management, and geometric modeling in Computer-Aided Design. The last two consist of fundamental disciplines required to understand the mechanics and physics of advance manufacturing processes, to develop mathematical and Professors and research scientists from the Center for Materials Technologies will introduce students to the Center laboratories, to ongoing research activities, and propose possible projects for Master's thesis research. This course will help students to select a specialization and future research advisors. |
Alexander Safonov | 3 | MA030296 | ||
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Introduction to Computational Mechanics in Energy Transition (Term 1B)
The course is a general introduction to the science and technologies related to energy transition and focuses on main research and educational efforts of the Center for Energy Transition. The faculty and researchers of the center will introduce their research areas and discuss ongoing projects. Representatives of industrial partners may also participate to introduce their research and development programs. All aspects of the educational program in Applied Computational Mechanics will be explained, including typical course structure, thesis requirements, summer internships, and others. Various technologies for energy transition will be introduced, and an overview of computational mechanics aspects of these technologies will be discussed. These include theoretical and computational mechanics of fluids and multi-phase systems and computational techniques for atomistic simulations for prediction of material properties. In all cases, students will be presented with projects that they can choose from as possible thesis research topics. Students will need to analyze several possible areas of research and submit reports on the chosen topics.
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Andrei Osiptsov | 3 | MA030565 | ||
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Introduction to Data Science (Term 1B)
The course gives an introduction to the main topics of modern data analysis such as classification, regression, clustering, dimensionality reduction, reinforcement and sequence learning, scalable algorithms. Each topic is accompanied by a survey of key machine learning algorithms solving the problem and is illustrated with a set of real-world examples. The primary objective of the course is giving a broad overview of major machine learning techniques. Particular attention is paid to the modern data analysis libraries which allow solving efficiently the problems mentioned above.
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Ekaterina Muravleva | 3 | MA030111 | ||
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Introduction to Life Sciences Program (Term 1B)
This is an introductory course aiming to give the students coming into the Life Sciences Program a birds-eye view of the research directions in the field of biomedicine at Skoltech. Soon after coming to Skoltech, the incoming students will be expected to join a lab and pick a research project. The objective of this course is to make this decision more informed.
The course will be presented by Skoltech faculty involved in life sciences research, mainly those of the three centers, CMCB, CNBR and Project Center for Agro Technologies. These faculties are prospective research advisors for Skoltech LS students. The lectures will cover the context for the research, present the labs, and showcase the research projects performed in the labs. The final schedule (what lecture reads which professor, and when) is read by the end of October. |
Vera Rybko | 3 | MA030371 | ||
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Introduction to Petroleum Engineering (Term 1B)
The course is an introduction to Petroleum Engineering and gives an overview of Petroleum Engineering and its various components and their internal connection.
The course will address the story of oil from its origin to the end user. The objective is to provide an overview of the fundamental operations in exploration, drilling, production, processing, transportation, and refining of oil and gas. As additional topics it is planned to consider Permafrost Engineering and Flow Assurance, which are actual for Russian Oil&Gas Industry. Within the framework of the course it is planned to invite speakers from industry. |
Alexey Cheremisin | 3 | MA030064 | ||
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Introduction to Quantum Groups (Term 1-2)
Quantum groups were introduced in the mid-80's and very quickly became one of the most important topics in mathematics and mathematical physics. They are still actively studied, and their knowledge is necessary for work in many areas.
The purpose of the course is an introduction to quantum groups. The content will be based on classic works of the 80's and early 90's, we will not get to the latest results. Initial knowledge about quantum groups is not assumed, but acquaintance with Lie algebras and Groups, Poisson brackets, and the first notions of category theory is assumed. |
Mikhail Bershtein |
63 per term
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MA060426 | ||
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Introduction to Wireless Communications (Term 1B)
The proposed course is an introductory course in the "Wireless Communications and Internet of Things" program and describes the theoretical principles behind the design and operation of wireless networks. The course gives an overview of wireless communication technologies, describes communication protocols, and describes the generations of cellular communication systems. The course also describes physical principles and models of signal transmission, propagation, and reception.
The course materials include the basic mathematical concepts and algorithms used in modern digital wireless networks: synchronisation, analog-to-digital and digital-to-analog conversion, modulation, and error-correcting coding. Lecture materials also include a mathematical reminder required to evaluate wireless communication algorithms: elements of probability theory, stochastic processes, and elements of linear algebra. The course consists of lectures, practical laboratory exercises, and homework. Labs assume numerical experiments and evaluation of wireless networks in some toy setups. The main programming language for laboratories is Python. Homework assignments may also include a theoretical analysis of wireless network protocols. The course is composed of seven main topics: 1) OSI reference model and generations of cellular systems; 2) Fourier transform; 3) Elements of Probability Theory; 4) Sampling of deterministic and random signals; 5) Wireless channel models; 6) Signal modulation and demodulation for different channel models; 7) Elements of coding theory. The main goal of the course is to select the material necessary for the successful study of the course (Digital Signal Processing, Information and Coding Theory, Fundamentals of Wireless Communication), as well as to enable students to conduct research on wireless networks. |
Kirill Andreev | 3 | MA030409 | ||
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Leadership for Innovators (Term 1B)
Successful innovators are distinguished not only by their scientific excellence as well as end user vision, but also by superior leadership skills. Scientists dream about being honored and awarded by fans on the merits of their science alone, but unfortunately it never works this way. Innovation is impossible without leading, cooperating, negotiating, and keeping resilience from the constant stress. This course presents the comprehensive skillset of leadership that includes theory and practice of: – leadership & teamwork – self-awareness and goal setting – stress management and self-presentation – empathy and 360 feedback – influence & negotiations The recurring topic of the class is that all these beneficial skills are fuzzy and overrated unless they are taken together in the globally accepted framework of "Emotional Intelligence" (EQ). The class is built as highly interactive action that starts with Q&A on a particular component of the EQ toolkit and then culminates in the intensive group and personal exercises. Considering the circumstances, we'll try to make this course as interactive as possible with your active participation in online class – sessions. Unlike your favorite hard skill classes, this course is lighter on homework, but harder on class participation. However, you will be given quite a number of home assignments, relating to self-awareness, emotional intelligence, and other leadership competences development.
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Maxim Kiselev | 3 | E&I | MC030011 | |
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Light-Matter Interaction Fundamentals (Term 1B)
The course describes the fundamentals of the interaction of light with matter within the framework of semi-classical and quantum physics. Suggested course is essential for future experimental work in laboratories and can be helpful also for theoreticians to grasp the idea on what can be measured in modern lab. We believe the course will be useful for undergraduate and PhD students to expand their expertise in modern optics, with some focus on experimental realization. The goal of the course is:
• to expand a general expertise of students in optics • to cover the gap in classical education implying only theoretical explanation without any details on real-world applications of the techniques. The course will cover such topics as interaction of light with two-level systems, basics of low dimensional semiconductors and devices, strong coupling of light and matter in microcavities and some aspects of nonlinear optics. |
Sergey Alyatkin, Yuriy Gladush |
3 | MA030594 | ||
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Materials Chemistry (Term 1B)
The goal of this course is to provide a survey of materials chemistry with a stress on chemical, electrical, optical and magnetic properties. Some materials synthesis methods will also be covered. Further emphasis will be placed on the application of materials chemistry to energy storage and conversion processes (batteries, fuel- and solar-cells, etc.)
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Stanislav Fedotov | 3 | MA030530 | ||
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Mathematics for Engineers (Term 1B)
The aim of this course is to refresh the basic topics that you are expected to get at bachelor level. If you do not feel confident with basic matrix manipulations, integration and differentiation, solving differential equations, operating with complex variables e t.c. – you should definitely take this course; fluency in these is a must for an educated engineer.
How to do it in 4 weeks? Sounds like impossible. Indeed, you will not recap each and every topic of your bachelor studies of math that took few years. But you'll have a chance to master some of the topics through specific engineering application. During three weeks you'll be given three sequential tasks associated with one application. Week 4 is devoted to summarizing the experience obtained and making final presentation. |
Elena Gryazina | 3 | MA030282 | ||
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Models of Sequential Data
In this course, we discuss the forefront of modern research in learning from sequence data. The course takes a walk from the basics of sequence processing to modern deep learning approaches. We aim at covering both fundamental and modern advances in this area not commonly discussed in undergraduate or graduate Machine Learning and Deep Learning classes.
Over multiple weeks, we will investigate how researchers and practitioners use these methods and algorithms for analyzing time-series data, text data, or medical sequences. The course aims to bring all students on the same page. They do not require severe background knowledge. The objective is to provide them with both depth and breadth knowledge of the state-of-the-art in sequence modeling. |
Alexey Zaytsev | 6 | MA060433 | ||
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Molecular Biology (Term 1B-2)
Molecular biology course is based on learning the principles of replication, recombination, DNA repair. Additionally, replication strategies of phages and viruses will be discussed. Mitosis and meiosis will be described in a context of DNA biosynthesis. Also, the principles of RNA biosynthesis, i.e. transcription and processing, as well as protein biosynthesis, i.e. translation, maturation and transport will be described.
The goal of the course is obtaining a comprehensive knowledge on the structure of DNA and processes of DNA replication, recombination and repair in bacteria and eukaryotes, as well as on replication of phages and viruses. Also, students will obtain a detailed knowledge on the processes of transcription, in bacteria and eukaryotes, on the regulation of transcription in bacteria and eukaryotes, on examples of complex networks of transcriptional regulation in bacteria and eukaryotes, on maturation of RNA in eukaryotes, on protein biosynthesis in bacteria and eukaryotes, on the transport of protein in bacteria and eukaryotes. Alongside with comprehensive knowledge on fundamentals of molecular biology, students will get a brief understanding of current problems in the field and modern methods for their solving. Students activities include: |
Petr Sergiev |
63 per term
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MA060034 | ||
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Neuroimaging and Machine Learning for Biomedicine
Nowadays Computational Neuroscience and Neuroimaging are fast-growing areas mostly due to new methods of acquiring, storing and processing of experimental data. Application of AI systems in clinics (for example, creating medical decision support systems) and in adjacent areas (education, pedagogics, etc.) includes processing of Neuroimaging data acquired from different devices (modern multichannel “dense” EEG systems, high-field MR-scanners, multichannel fNIRS systems, which allow precisely and non-invasively record brain activity with good spatial and temporal resolution), automation of these data analysis, and new knowledge extraction from it. The majority of relevant tasks include: classification tasks for diagnostics and prognosis, finding clinically (biophysically) significant patterns, highlighting areas of interest and others.
Neuroimaging data has several distinctive properties: it is multimodal, high-dimensional and usually very noisy. An effective analysis of these data requires understanding of the biophysical processes in the organism and the processes occurring in the scanning equipment, which are both reflected by neuroimaging data, as well as the use of a number of mathematical models that adequately describe these processes. The aims of the course are: • to explain basic ideas and results in tasks and approaches for the neuroimaging data preprocessing based on biophysical principles and processes in scanning equipment, • to give a review of various mathematical models describing the neuroimaging data reflecting their specific properties, • to teach the student how to use the conventional or modified Data Analysis and Machine Learning techniques for extracting meaningful biomarkers from the data and solving fundamental neuroscience problems as well the applied biomedical tasks. |
Maxim Sharaev | 3 | MA030421 | ||
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Parallel Computing in Mathematical Modeling and Data-Intensive Applications (Term 5-6)
This interdisciplinary course:
— makes the students familiar with main scientific and engineering applications of modern supercomputers, — explains numerical methods behind the applications and their implementation — discusses efficiency of the common algorithms on modern supercomputer architectures — extends students background in modern processors and supercomputer architectures The applications includes computations fluid dynamics with finite difference and finite volume methods, Lattice Boltzmann and cellular automata, finite elements modeling, molecular simulations, plasma, quantum chemistry, distributed deep learning on multiple computing devices, processing big volumes of data (e.g. large graphs) on distributed systems. Each topic includes a lecture by a lead instructors, invited high-profile guest lecturers and students. Each lecture is devoted to a particular application. Students will form teams to work on projects in one of the application areas and then share their experience with the fellow students at seminar sessions and a final project presentation at the conclusion of the course. |
Sergey Rykovanov |
63 per term
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MA060411 | CANCELLED | |
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Path Integral: Stochastic Processes and Basics of Quantum Mechanics (Term 1-2)
One of the most powerful methods of modern theoretical physics is the method of functional integration or path integration. The foundations of this approach were developed by N. Wiener at the beginning of the 20th century, but it spread widely after R. Feynman, who applied this approach in quantum mechanics. At present, the functional integral has found its application in the theory of random processes, polymer physics, quantum and statistical mechanics, and even in financial mathematics. Despite the fact that in some cases its applicability has not yet been mathematically rigorous proven, this method makes it possible to obtain exact and approximate solutions of various interesting problems with surprising elegance. The course is devoted to the basics of this approach and its applications to the theory of random processes and quantum mechanics. In the first part of the course, using the example of stochastic differential equations, the main ideas of this approach will be described, as well as various methods for exact and approximate calculation of functional integrals. Further, within the framework of the course, the main ideas of quantum mechanics will be considered, and both the operator approach and the approach using functional integration will be considered. It will be demonstrated that, from the point of view of formalism, the description of random processes and the description of quantum mechanical systems are very similar. This will make it possible to make a number of interesting observations, such as, for example, the analogy between supersymmetric quantum mechanics and the diffusion of a particle in an external potential. In the final part of the course, depending on the interests of the audience, various applications of the functional integration method will be discussed, such as polymer physics, financial mathematics, etc.
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Andrey Semenov |
63 per term
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MA060542 | ||
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course describes the intended learning outcomes and how they are assessed.
The main bulk of the 81 hours of the course is spent in the actual courses in which the PhD-students do their TA-assignments. The assignments in the course itself include TA proposal and TA report and for cohorts 2022-2025 participation in training: Towards excellence in Teaching Assistantship”. Educational department should approve all assignments in Canvas in terms of content and formal requirements respectively. |
Dmitry Artamonov | 3 | DG030005 | ||
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Pedagogy of Higher Education (Term 1B)
The course offers an introduction to facilitating learning in higher education for PhD students asked to act as teaching assistants. The course content focuses on high resolution constructive alignment of learning outcomes with learning activities and assessment strategies. Learning outcomes for a course are elaborated into separate activities and assignments for students. Learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. Participants in the course will therefore be required to plan and design effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments. The course therefore emphasizes communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course explores ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching assistantships or their supervisory activities to come. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Daria Tokmeninova | 3 | DG030025 | ||
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Photonics Research Seminar Series (Term 1B-4)
The course "Photonics Research Seminar Series" aims to provide MSc and PhDs with a broader perspective into state-of-the-art research in photonics, from fundamental insights (both theoretical and experimental) to engineering and applications. The course also aims to train students in how to structure scientific results, how to provide a coherent narrative and how to appropriately present it in the form of a strictly-timed oral presentation. The bulk of the course consists of a weekly one hour seminar on seminal topics of photonics, from guest speakers or Skoltech faculty, researchers and students.
The course is a prerequisite for the research thesis defense of projects of the following laboratories (and is as such compulsory only for the 2nd year MSc students of these laboratories): |
Pavlos Lagoudakis |
30.75 per term
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MA030553 | ||
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Plant Genetic Diversity and Adaptation to Stress (Term 1B)
Plants, unlike animals, are rooted in a single location and thus forced to adapt to any environmental conditions. In order to withstand stressful conditions, plants have evolved an array of advanced physiological, biochemical, and molecular defense mechanisms, which help them in avoiding or combating negative environmental effects. Climate change is expected to exacerbate unfavorable environmental conditions caused by abiotic (drought, flooding, salinity…) and biotic (pests, diseases) stress factors, resulting in sharp reductions in average crop yields. On the other hand, societal demand for environmentally friendly farming practices with low inputs supports the need for new improved varieties tolerant to harsh environmental constraints. Understanding plant responses and adaptation mechanisms to extreme stress conditions is therefore critical to the genetic improvement of economically important crops.
Implemented in a project and problem-based learning approach, this teaching unit is built around an experimental project as a common thread throughout the course, addressing a current hot-scientific agronomic or ecological issue linked to the adaptation of plants to a key environmental constraint. |
Cecile Ben | 3 | MA030479 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Nikolay Brilliantov | 3 | DD030020cd | ||
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Qualifying Exam: Engineering Systems
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Henni Ouerdane | 3 | DD030020es | ||
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Qualifying Exam: Materials Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Artem Abakumov | 3 | DD030020ms | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Mikhail Spasennykh | 3 | DD030020pe | ||
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Qualifying Exam: Physics
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Nikolay Gippius | 3 | DD030020p | ||
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Quantum Mechanics (Term 1B)
The course will review the basic concepts of quantum mechanics. It is intended both for those who studied quantum mechanics previously and for those who did not. The purpose of the course is not only to introduce the main principles of quantum mechanics but to familiarize with them through active problem solving, which is the only practical way to study quantum mechanics. The course will cover the main topics such as one-dimensional motion, perturbation theory, scattering theory, approximate methods in quantum mechanics, density matrix formalism.
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Sergey Dyakov | 3 | MA030177 | ||
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Reinforcement Learning
Reinforcement learning is a vanguard method of machine learning aimed at dynamical applications, ranging from video games to autonomous cars, robots, drones etc.
Composed of a so-called agent and environment, it is meant to resemble, in a sense, the behavior of living beings. Agents interact with the environment and optimize their actions to improve rewards. Imagine a video game speed run. An agent, the protagonist, interacts with the game environment and wants to beat the game as fast as possible, by dynamically adjusting his or her controls while learning on-the-fly. Reinforcement learning is truly an interdisciplinary subject that can be studied from different perspectives — machine learning, control theory, dynamical system theory, pure math etc. In this course, we dive into reinforcement learning for studyng the key principles and implementing them on real examples. |
Pavel Osinenko | 6 | MA060422 | ||
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Representations of Finite Groups (Term 1-2)
Representation theory is used in many areas of mathematics (algebra, topology, algebraic groups, Lie groups and Lie algebras, quantum groups, algebraic number theory, combinatorics, probability theory, …), as well as in mathematical physics. Therefore, mastering the basic technique of representation theory is necessary for mathematicians of various specialties. The aim of the course is to give an introduction to representation theory on the material of finite groups. Particular attention will be paid to representations of the symmetric groups.
Tentative program: 1. Reminder of the basics from the algebra course: group algebra of a finite group, irreducible representations, Schur's lemma, characters, orthogonality relations, Maschke's theorem, Burnside's theorem 2. Representations of finite Abelian groups, duality for finite Abelian groups, Fourier transform, biregular representation 3. Intertwining operators, induced representations, Frobenius duality 4. Mackey machine, projective representations, coverings over symmetric groups 5. Functional equation for characters, Gelfand pairs, spherical functions, connection with orthogonal polynomials 6. Representations of the symmetric group: various approaches to the classification and construction of irreducible representations 7. If time permits: principal series representations for the group GL(N) over a finite field, Hecke algebra, Harish-Chandra theory |
Grigori Olshanski |
63 per term
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MA060595 | ||
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Research Seminar "Advanced Materials Science" (Term 1B-4)
This is a research seminar of the Skoltech Center for Energy Science and Technology and Materials Science Education program featuring presentations of young Skoltech researchers (MSc students, PhD students, postdocs) as well as external invited speakers. Every MSc and PhD student should deliver at least one presentation during the course. The range of topics is broad and includes any aspects of materials science. As a rule, each speaker presents results of his/her own research with a particular focus on research methodology. Every presentation is followed by a discussion.
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Sergey Luchkin |
30.75 per term
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DG030302i | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, cluster algebras etc.
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Andrey Marshakov |
61.5 per term
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DG060268 | ||
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Scientific Computing (Term 1B)
This is an introductory course in Scientific Computing, with a focus on the mathematical and algorithmic aspects of real-world computations in various areas such as physics, mathematics, medicine, economics, and more. The course comprises both theoretical and practical components.
The theoretical branch introduces the fundamental concepts of various types of mathematical problems that arise in different applications. It also covers approaches to classifying these problems, reformulating them when necessary, and providing solutions. The practical aspects involve the application of various computational techniques for solving scientific and engineering tasks. These techniques will be taught through practical demonstrations, and efforts will be made to integrate them as much as possible with the corresponding theoretical materials presented during lectures. All topics in the course will be covered at an advanced introductory level. The goal is that, upon completing the course, students will have acquired enough knowledge to begin using scientific computing and high-performance computing (HPC) methods in their everyday research work. Students should have a comfortable understanding of undergraduate mathematics, particularly in the basics of calculus, linear algebra, and probability theory. Some preliminary knowledge of Unix-like operating systems is a plus. While the course will provide an overview of some popular pieces of commercial software used in HPC, all the software used for practical tasks in this course is open source and freely available. |
Nikolay Koshev | 6 | MA060113 | ||
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Seismic Tomography
The aim of the course “Seismic Tomography” is to present different seismic tomography methods, to provide theoretical basis of the tomographic inversion and to teach performing practical work with synthetic and experimental data. The course “Seismic Tomography” is mainly oriented to students of geophysical specialty; however, it is also suitable for specialists in adjacent disciplines of geological and physical sciences. The course starts with a historical overview. Then the theoretical basis of the tomography inversion is introduced with the descriptions of the major elements, such as linearization, parameterization and regularization. This part is supported by practical exercises with a simplified version of the tomography code, BASIC_TOMO. Then after describing the ray tracing algorithms, we will start working with the active-source tomography code PROFIT, which is used as an instrument for many practical and scientific applications. In the following section, we present a problem of earthquake locations and learn the LOTOS code for local earthquake tomography. Within this section, we will perform practical exercises based on several experimental datasets. In the next section, we describe the seismic surface waves and the basic principles of ambient noise tomography. We will use the surface-wave tomography code to process several experimental datasets. We will also consider the tomography methods to study seismic anisotropy, attenuation and to reveal temporal velocity changes. A prospective direction of seismic imaging is Full Waveform Inversion (FWI), whose basic elements will be introduced. An important element of any tomography study is verification, which should present solid arguments that the constructed seismic velocity anomalies do really represent the actual geological structures inside the Earth.
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Ivan Kulakov | 3 | MA030562 | CANCELLED | |
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Soft Condensed Matter (Term 1B)
The course is tailored to (1) Computational Science students (especially DIMMS track), as it introduces to Machine Learning application to physics (2) Petroleum Engineering students, since emphasized in all sections are applications in hydrocarbon recovery and processing
The course introduces students to the physical, engineering and modeling aspects of soft systems, that is the systems that can be structurally altered by forces comparable to thermal fluctuations in magnitude. Soft matter is abundant in industry (oil recovery and processing, detergents, adhesives, etc) and in biosystems, including the very human body. Specific topics will include interactions in emulsions and dispersions, stability of thin films and colloids, rheology, and aggregation dynamics. The emulsion block is followed by the behavior of amphiphiles, self-assembly, liquid crystals and colloids stabilization. Soft polymeric structures are next, and the final part will feature foams. The exact program will be adjusted depending on the background of the audience. Out of two optional topics (foams and lipids) one will be offered for self-study and course projects, depending on the audience. The course will be useful to all students willing to improve their understanding of natural (e.g. mineral oil) and man-made colloids and polymers. The course will also provide a hands-on experience in data-driven modelling of soft matter gained via in-class labs, homeworks and term projects. Term project will involve both physics-driven and data-driven approaches, on students choice. |
Alexey Vishnyakov | 3 | MA030365 | ||
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Structural Optimization
The course is intended to give basic knowledge and skills how to formulate a structural optimization problem, including defining appropriate design variables, constraints, and objective functions. The main topics of the course are classification of structural optimization problems, calculus of variations and energy principles in solid mechanics, structural approximations, sensitivity analysis, optimality criteria, shape optimization, structures of maximum stiffness and topology optimization, SIMP-method. Computer exercises on structural optimization will be performed.
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Alexander Safonov | 6 | MA060452 | ||
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Symplectic Geometry (Term 1-2)
Symplectic geometry is a kind of skew-symmetric analogue of Riemannian geometry. This domain of differential geometry serves as a geometric basis of calculus of variations, quantum and classical mechanics, geometric optics and thermodynamics. The language of symplectic geometry is used everywhere in modern mathematics: in the Lie group theory, the theory of differential equations, integrable systems, singularity theory, topology. The course will cover both basic concepts of the theory and some specific problems needed for particular applications.
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Maxim Kazarian |
63 per term
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MA060596 | ||
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Technology Entrepreneurship Seminar: Foundation (Term 1B-2)
The course is designed to help you to learn and master practical skills of technology entrepreneurship and to accelerate your startup projects up to “external support/funding ready” level. It is intended for students: (1) interested in new tech venture creation and technology entrepreneurship; (2) having their own projects/ideas in development; (3) planning startup contest participation, pitch to investors, applying to Triple Point Contest, FASIE (“Bortnik Fund”), STRIP, accelerator/incubator program, etc. The startup project concept may be in the early experimental mode, or further along in its evolution such as seeking customers or pilot tests.
The course is conducted as practical and hands-on lab. We will not study entrepreneurship as a theoretical subject through external cases or papers. The material for learning comes directly from the class projects and the issues faced by students in converting their ideas into successful ventures. This assures projects real development and creates a highly dynamic environment for teaching where the faculty is a facilitator, mentor, tracker, and lecturer at the same time. The course subject areas represent “golden standard” for tech startups: (1) Problem and Market Need; (2) Product and Technology; (3) Market opportunity evaluation; (4) Business Model; (5) Startup Team; (6) R&D/Marketing/Sales/Funding plan; (7) Storytelling and Pitching. Upon the course completion, you and your team will The teams interested in the further development of their projects can continue seamlessly by entering the course "Technology Entrepreneurship Seminar: Advanced" (E&I track, 3 credits, Terms 3 & 4). |
Alexey Nikolaev |
31.5 per term
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E&I | MC030029a | |
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 |
Term 2
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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Academic Communication: Preparatory English for PhD Exam
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities to be performed in class and at home, individually and in teams. By the end of the course, successful participants will |
Elizaveta Tikhomirova | 3 | Extra | DF030029 | |
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Advanced Fluid Mechanics: Multiphase Flow Modeling in Energy Transition
The course is devoted to mathematical modelling of multiphase flows with a particular focus on technological processes of energy transition and oilfield services. Key concepts to be covered are as follows: energy transition; classification of multiphase flows; key approaches to describe multiphase flows (direct numerical simulations Vs. continuum); conservation of mass, momentum and energy in two-continua approach; rheology of particle-laden flows; forces acting on particles in unsteady shear flows; hydrodynamic similarity of multiphase flows and non-dimensional parameters; classification of instabilities at fluid-fluid interfaces; multiphase flows and particle transport in porous media. Mathematical models of the following technological processes related to energy transition are formulated: geological storage of carbon dioxide; fluid-fluid displacement in a pipe and cylindrical annulus during well cementing; proppant transport in hydraulic fractures; fracture cleanup; overview of mathematical modelling issues related to long-term oil production.
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Sergei Boronin | 6 | MA060571 | ||
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Advanced Manufacturing of Composite Materials
This course is developed to give students a broad background and hands on experience in manufacturing of advanced composite materials. Both materials and manufacturing methods are discussed. A brief introduction to advanced composite materials and processes is presented. The course is focused on the innovative non-autoclave technologies of thermosetting resin based/fiber reinforced advanced composites. Manufacturing is covered in terms of the major steps required to fabricate laminated composite parts. These will be described and discussed in details and worked out experimentally through conducting a set of lab projects. The following technologies and methods will be covered: Vacuum Infusion, Press Molding, Pultrusion, Filament Winding, and Mechanical Testing. Typical problems of materials, tooling, cure, and technological defects will be discussed.
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Alexander Safonov | 6 | MA060298 | ||
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Aerosol Science and Technology
The course will introduce the basic phenomena of aerosol science, particle formation in the gas phase and their behavior, concepts and measurement techniques for the aerosol particles. Students will synthesize (carbon nanotubes, NaCl, metal, metal oxide and polymer) nanoparticles by two aerosol techniques: gas-to-particle and liquid-to-particle conversions. Students will be trained to operate spark-discharge aerosol synthesis reactor for production of nanoparticles and single-walled carbon nanotubes and spray drying and pyrolysis reactors.
The student will perform the on-line measurements of number size distribution of aerosol synthesized nanoparticles by differential mobility analyzer (size range: 2-1000 nm). Students will become familiar with processes of the aerosol particle collection (filtration, electrostatic precipitation, thermophoretic precipitation). The produced samples of nanoparticles will be observed with means of transmission and scanning electron microscopies. Totally 34 lecture hours and 15 exercise hours, 5 hours for seminar lessons, 6 presentation hours will be arranged. Students will write a short essay and give a presentation on one of the selected topics. |
Albert Nasibulin | 6 | MA060300 | CANCELLED | |
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Bayesian Methods of Machine Learning (Term 1B-2)
The course addresses Bayesian approach for solving various machine learning and data analysis problems. It offers the framework for solving inverse problems, model selection and continual learning. The most attention is payed to the fusion between the deep learning techniques and the elements of bayesian approach. This fusion is possible due to the development of a range of approximate inference techniques in the last 20 years.
Modern machine learning papers frequently use machinery of the approximate inference techniques. The main learning outcome of the course is the ability to read and reproduce related papers, and to apply corresponding methods of approximate inference for the development of Bayesian machine learning approaches. In order to reach this goal, the course contains theoretical and practical assignments and the final project. The purpose of the theoretical tasks is two-fold. Firstly, we would like to develop skills of equation derivation, that is used routinely in papers and is usually suppressed. Secondly, some theoretical tasks provide the intuition about properties of the methods through toy models. The purpose of the practical tasks is straightforward: to translate discussed methods to the code and observe properties of the methods through examples. These assignments leads to the course projects: reproducing and discussing a relevant paper from a recent conference, and/or development of some students' research ideas using methods of Bayesian machine learning. |
Evgeny Burnaev |
63 per term
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MA060129 | ||
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Bioinformatics
This course is aimed for the first year master students with no prior knowledge in bioinformatics. The main course idea is to get students from different backgrounds acquainted with basic tools and algorithms in bioinformatics, such as basic phylogenetic analysis, algorithms behind sequence alignments, protein structure analysis, gene and genome annotation, as well as main databases storing sequence, protein and epigenetics data.
The knowledge obtained during this course can be immediately utilized in the real biological and computational projects. |
Mikhail Gelfand | 6 | MA060307 | ||
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Biomedical Innovation and Entrepreneurship
This course aims to provide students with an understanding of applications and practices of biomedical sciences in social healthcare. To put it simple, we will discuss where and how Skoltech biomedical graduates may employ their skills beyond academy science. To achieve this goal the course will decompose the industry into the value chain of independent but interconnected entities and then make deep investigation of motives, profits, and costs of any segment/entity of this value chain. The incomplete list of such entities will include: R&D-driven startups, CROs, CMOs, regulators, integrated pharmas, marketing agents, distributors, retail, hospitals, doctors. The emphasis will be made on the value chain groups that are immersed into the challendge of transforming high technologies into the tangible patient benefit, from hardcore drug development to all kinds of medical devices and services. Such challenges will be taught through development of the group project that will be developed through the stages of Problem statement (indication, regulation, POC and QC), preclinical design, clinical design, manufacturing/delivery design and final integrative presentation.
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Dmitry Kulish | 3 | E&I | MC030013 | |
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Biomedical Mass Spectrometry
This course introduces students to the first principles and methods of mass spectrometry with special emphasize on biological and medical applications. The course will cover wide range of mass spectrometry techniques used for ion generation, separation, detection and data processing and interpretation. The course will teach the theoretical fundamentals required for the design of instruments and methods for measuring mass spectra of biological samples. The course will cover mass spectrometry applications in OMICs technologies, in biomarker discovery and in tissue imaging.
After successful completion of this class, students will acquire the initial knowledge of the operational principles and design of different mass spectrometers, different methods of ionization of biological molecules of wide mass range, different methods of ion separation including magnetic sector, time of flight, RF and DC ion traps, as well as FTICR. Experimental and bioinformatics-based methods of protein, peptides, lipids and metabolite molecule identification, different fragmentation methods for primary and secondary structure determination, methods of quantitative determination of proteins, lipids, metabolites and small molecule in physiological liquids. |
Evgeny Nikolaev | 3 | MA030256 | ||
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Biophysical Chemistry: Methods and Applications
The Introduction to Optical Biosensors course gives students the basic concepts of biosensors with specific focus on optical biosensor technology. The course underlines the following important directions: 1) optical transduction approaches, such as luminescence, absorption, surface plasmon resonance, etc. that can be used for the detection and identification of chemical and biological species; 2) biological recognition elements that can be used for the development of biosensors and their interactions for the detection of analytes; 3) the applications of nanomaterial and nanotechnology for fabrication of optical biosensors. The course describes broad range of application of optical biosensors for the detection of various analytes.
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Alexey Yashchenok | 3 | MA030580 | CANCELLED | |
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Biotechnology and Crop Improvement
The course will focus on the biotechnological methods and laboratory skills required for successful implementation of modern genome editing and genetic transformation technologies into plant breeding. The training module will begin with an introduction the basics of in vitro plant manipulation in aseptic conditions, e.g. tissue culture, explants, callus types, composition and preparation of media, conditions for successful regeneration, necessary equipment. Then, the course provides theoretical knowledge and practical training in the field of crop transformation using Agrobacterium, isolation of plant protoplasts for transient expression of foreign genetic material, bioballistic transformation of calli and immature embryos. The principles of guide RNA design for genome editing will be discussed, as well as types of editing (nucleases, base editors, Prime editors), methods for delivering a CRISPR/Cas9 construct to plant tissues. With the example of monocot and dicot plant species, protocols of vectors design for particle gun and Agrobacterium, main cloning strategies, selectable markers, marker genes (GFP, GUS) will be demonstrated.
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Elena Potokina | 6 | MA060587 | ||
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Cancer Biology
The course is dedicated to basics of clinical and molecular cancer biology with emphasis on innovative drugs and technologies for cancer treatment and diagnostics.
The main themes to be discussed: What is cancer? Classification, staging, grading, еhe hallmarks of cancer; Mutations, oncogenes, tumor suppressors, пenomic instability, epigenetics in cancer initiation and progression (miRNA, siRNA, lncRNA, methylation, histones modification, etc.); Cell cycle and growth and it’s deregulation in cancer; Tumor energy metabolism (the Warburg effect); Cell death and it’s deregulation in cancer (necrosis, apoptosis, autophagy); Cell differentiation and dedifferentiation in cancer, EMT, Metastasis; Cancer stem cells hypothesis; Tumor stroma and heterogeneity, neoangiogenesis; The “seed and soil hypothesis” – pre-metastatic niche; Extracellular vesicles for communication between cells; Cancer diagnostics, tumor markers; Liquid biopsy; Biological factors in Cancer (inflammation, viruses, bacteria, microbiome); Target therapy, Immune checkpoint inhibitors; Emerging therapeutic modalities, Car-T, dendritic cells, viruses; |
Vera Rybko | 3 | MA030088 |
MOVEDmoved to Term 3
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CDE Seminars (Term 2-4)
This is the main research seminar of the Skoltech Digital Engineering Center featuring presentations of young Skoltech researchers (MSc students, PhD students, postdocs) as well as external invited speakers. Every MSc and PhD student should deliver at least one presentation per two years. The range of topics is broad and includes many aspects of Digital Engineering, Systems Engineering, Product Development, Robotics and Automation and Space Research and Systems. As a rule, each speaker presents results of his/her own research. Every presentation is followed by a discussion and a Q&A session.
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Clement Fortin |
31 per term
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MA030606 | ||
| Cluster Varieties and Integrable Systems (Term 1-2) | Andrey Marshakov |
63 per term
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MA060597 | ||
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Computational Biology of Aging
This course will be devoted to the study of key methods of computational biology of aging, which have had a significant impact on the field in recent years. We will take a detailed look at aging clock models, survival curves, Gompertz models, dynamical models as well as key aspects of the biology of aging (including its molecular part). Students will get experience in differential gene expression or methylation analysis in the context of aging, hazard ratio models fitting and interpretation, aging clocks construction and their exploration and develop an intuition in dynamical systems modeling. The course aims to form a systematic view of the biology of aging, and offer a holistic computational methodology for the study of this problem.
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Ekaterina Khrameeva | 3 | MA030511 | ||
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Computational Chemistry and Materials Modeling
The course provides a graduate level overview of modern atomistic computer simulations used to model, understand and predict properties of technologically important materials. The emphasis is on practical use of techniques, algorithms and programs to bridge theory and applications, from the discovery of materials to their use in real-world technologies. Several laboratories give students direct experience with simulation methods as well as practical knowledge on how to use computational modeling and how to present and interpret results of simulations. Bridges from atomic to complex systems demonstrate potential of different theories to applications relevant to multiple major industries in the future, including nanotechnology and energy.
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Dmitry Aksenov | 6 | MA060008 | ||
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Computational Materials Science Seminar (Term 1B-2)
This is the main research seminar at Skoltech for Computational Materials scientists. All students of Computational Materials Science subtrack of Materials Science MSc program and Materials Science and Engineering PhD program should attend this seminar. Topics include materials modeling (at atomistic scale), theoretical and computational chemistry, theoretical and computational physics of materials, underlying mathematical methods and algorithms etc. Invited lectures are top scientists in their research field. Students are welcome to present their own research results at this seminar and expected to do this at least once per two terms.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/ |
Dmitry Aksenov |
30.5 per term
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MA030430 | ||
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Computational Methods in Atomistic Simulations
Course will cover both classical and modern topics of computational methods in atomistic simulations of condensed matters. We will begin from classical representations of electric and heat transport in condensed matter and will finish with modern theories covering the molecular dynamics methods coupled with density functional theory and applications of machine learning. Practical part of the course will be devoted to hands-on sessions where students will apply these theories on practice by performing calculations of real materials. This will give an entire picture of applied computational methods allowing the solution of various tasks like calculations of mechanical properties of solid-state compounds, lattice dynamics, thermal conductivity etc.
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Aleksandr Kvashnin | 6 | MA060573 | ||
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Computational Thermodynamics and Kinetics for Materials Design
Unlock the power of computational thermodynamics and kinetics in materials design with our intensive course. Dive deep into the world of phase modeling, thermochemical evaluations, and alloy system analysis. Learn how to harness cutting-edge software like MatCalc, JMatPro, and Thermo-Calc to transform raw data into actionable insights. This course bridges the gap between theory and practice, blending rigorous academic study with hands-on workshops and independent research projects. You'll not only master the principles of thermodynamics but also learn how to apply them in real-world scenarios. By the end of the course, you'll be able to read and extract information from scientific literature, use ab initio calculated information in thermodynamic evaluations, and write a comprehensive scientific report.
The course turns to Msc2 and PhD students who want to get into basic thermodynamic modelling and calculations. |
Igor Shishkovsky | 3 | MA030525 | CANCELLED | |
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Continuum Mechanics (Term 2-3)
Continuum mechanics is a section of mechanics and theoretical physics, or rather the continuation of theoretical mechanics that deals with analysis of deformable bodies. However, mathematics in continuum mechanics represents the main constructive tool. Continuum mechanics allows to demonstrate the power of logic and mathematical thinking. Based on a few fundamental postulates and principles, using the mathematical apparatus can reveal non-trivial, and even striking results.
Foundation of continuum mechanics consists of: This course uses tensor representations in the Cartesian coordinate system of the observer. But it will one shown in detail how to to write the continuum mechanics equations in the arbitrary curvilinear coordinate system. This way the common link is not lost and the exposition becomes easier and clearer. |
Robert Nigmatulin |
63 per term
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DA060181 | ||
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Convex Optimization and Applications
This course introduces the basic theory in convex optimization and illustrates its use in recent successful applications such as sparse learning, blind source separation, low-rank optimization, image processing, regression and classification, phase retrieval.
Through the course, students will study convex sets and functions and their properties, duality and dual maximization problem with the same optimal value, a certificate of optimality for an optimization problem. Students will also learn some commonly known convex optimization forms such as Linear Program, Quadratic Program, Second Order Cone Programs, Semi-Definite Programs etc. Students will know practical tools, and able to recognize and formulate convex optimization problems and solve them using efficient solvers. |
Anh Huy Phan | 3 | MA030136 | ||
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Crystal Chemistry
This course will be useful to a diverse group of students involved in designing new functional materials including but not limiting to electrode materials and solid electrolytes for rechargeable batteries. This course has two objectives: (i) to familiarize with the key parameters and laws that govern the organization of crystalline matter and related crystal structures, (ii) to give a comrehensive guide of to how to "read", understand and "tune" crystal structures to design, create and modify functional properties of materials.
Students will learn what is required to understand the potential of a new structure to become a base for a prospective material. The first part of the course will cover general structure types, their organization and pecularities, reveal essential crystal chemistry concepts, rules and laws. With these knowledge at hand, the students are invited to the second part of the course to be acquainted with the crystal structure organization of modern electrode materials for rechargeable batteries. The acquired knowledge and skills are expected to facilitate the creation of completely new materials with outstanding characteristics that find applications in technological problems related to energy storage and conversion. The content of the course is not exclusively fundamental but relates to practical aspects of the creation of innovative materials via a crystal chemistry design approach. |
Stanislav Fedotov | 3 | MA030477 | ||
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Differential Topology (Term 1-2)
The course will cover two topics, which are central in topology of smooth manifolds, the de Rham cohomology theory and Morse theory. The course will culminate in two famous results of differential topology: Smale's h-cobordism theorem and Milnor's discovery of exotic smooth structures on the 7-dimensional sphere. The h-cobordism theorem proved by S. Smale in 1962 is the main (and almost the only) tool for proving that two smooth manifolds (of dimension greater than or equal to 5) are diffeomorphic. In particular, this theorem implies the high-dimensional Poincare conjecture (for manifolds of dimensions 5 and higher). Milnor's discovery of exotic smooth structures on the 7-dimensional sphere and further results of Kervaire and Milnor were the first steps towards surgery theory, which is the most powerful tool for classifying smooth manifolds.
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Alexander Gaifullin |
63 per term
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MA060599 | ||
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Digital Signal Processing
Digital signal processing (DSP) refers to various techniques for improving the accuracy and reliability of digital communications. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, statistical signal processing, estimation theory, signal sampling, re-sampling, and reconstruction free of errors. We will analyze signal distortions due to sampling, interpolation, and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. We will also deal with modulation and multipath propagation channel models. Hands-on examples and demonstrations will be routinely used to close the gap between theory and practice.
The extra topics covered in this course are: – Spectrum analysis; – Machine learning for signal processing; – Fundamentals of random signal theory and analysis; – Modeling communication signals as random processes; – Baseband signal processing, signal synthesis, and filter design for communication; – Statistical signal processing in communication; It is hoped that through learning this course students will be equipped with a clear picture of DSP as well as a necessary foundation for further study of advanced DSP topics in the future. |
Andrey Ivanov | 6 | MA060255 | ||
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Engineering Design Factory - Part I (Term 5-6)
This course seeks to emulate real world product development process. It is a project based hands-on course and it is aiming to allow students to develop a concrete product for the market; they will learn and experience a detailed product development cycle including
its modelling, business case, industrial design, prototyping and testing in both the virtual and physical realities. The course comprises a series of lectures on product development methodologies and on the integration of the various dimensions to successfully achieve a product or system demonstrator with a credible market value. Products are assumed to consist of electrical, mechanical and data analysis components. Teams of 4 or 5 students with various backgrounds will be expected to work with industrial clients or bring their own product proposals to develop a valuable product or system starting from a Technology Readiness Level (TRL) of 2 or 3, and aiming to reach a TRL level of 4 or 5. The teams will present their product to external experts and stakeholders; the best products will be promoted by Skoltech to seek external funding. |
Clement Fortin |
63 per term
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MA060603 | ||
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English. Candidate Examination
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research structured according to the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
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Entrepreneurial Strategy. Section A
This course focuses on how scientists and technology entrepreneurs identify, design and implement strategies to sustain and enhance the success of the commercialization of their discoveries, examining issues central to the long- and short-term competitive position of the scientific and technological discoveries and developments they are willing to commercialize and transfer to society.
As a field, Strategy attempts to explain why and how some organizations outperform others in the marketplace, developing competitive advantages around technology, science or, overall know-how, and sustaining these advantages over time while remaining competitive. The course provides a set of frameworks and analytical tools that enable scientists and technology entrepreneurs to understand and plan effective strategies for competing with their technologies in a range of industries. |
Alexander Chekanov | 3 | E&I | MC030023a | |
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Entrepreneurial Strategy. Section B
This course focuses on how scientists and technology entrepreneurs identify, design and implement strategies to sustain and enhance the success of the commercialization of their discoveries, examining issues central to the long- and short-term competitive position of the scientific and technological discoveries and developments they are willing to commercialize and transfer to society.
As a field, Strategy attempts to explain why and how some organizations outperform others in the marketplace, developing competitive advantages around technology, science or, overall know-how, and sustaining these advantages over time while remaining competitive. The course provides a set of frameworks and analytical tools that enable scientists and technology entrepreneurs to understand and plan effective strategies for competing with their technologies in a range of industries. |
Alexander Chekanov | 3 | E&I | MC030023b | |
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Experimental Design in Biology
Reliable phenotypic data at
* the ‘macro’ level such as plant’s fitness, flowering time, bacterial colony growth or * the ‘micro’ level such as cellular response or pathogen colonization within tissues or * the ‘molecular’ level such as gene expression levels or metabolite profiling; are required for in-depth genetic studies. Due to the unique particularity to possibly replicate the same genotype in time and space, the phenotypic data of plants and microbes can be analyzed with strong power and accounting for variability. The design of experiments is all about learning as much as possible from the smallest amount of data. During this course, the fundamentals of determining the effects of given factors or covariates on a phenotypic trait will be given. This includes how to evaluate controlled and uncontrolled variability using the appropriate experimental design. The course is mostly practical-driven and aims to provide the skills to use these methodologies in future professional activities. |
Laurent Gentzbittel | 3 | MA030480 | ||
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Experimental Optics I
This introductory experimental optics course focuses on providing students with practical experience around some basic concepts and practices in the area of optics. It is designed to accompany theoretical courses that are offered to MSc students in the first terms of their study.
The course is centered around four experimental projects: polarization; a very important property of light and introduction to fundamental measurement techniques, Fourier Optics; a basic concept relevant to optical instrumentation, coupling to fibers and waveguides; providing an introduction to integrated optics and optical communications, and Optical interferometry; demonstrating the wave nature of light and providing an introduction to modern measurement procedures. The course includes a general introduction to lasers and optoelectronics and regular lectures which are relevant to the specific topics of the experiment. The students will work semi-independently in small group of 2-3 students with supervision from teaching assistants. |
Sakellaris Mailis | 3 | MA030521 | ||
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First Steps to Thesis in English (Life Sciences)
Writing is the key priority and the need of utmost importance for all would-be scientists. Science demands good writing, that presupposes the skills to communicate ideas, theories, and findings as efficiently and clearly as possible. Science lives and dies by how it is represented in print and printed material is the final product of scientific endeavour. The primary goal of this course is to prepare master students for wiring, editing, and defending a Master Thesis.
This course is designed to explain how to write chapters of their Thesis through practical examples of good writing taken from the authentic linguistic environment in life sciences. The course teaches how to overcome certain typical problems in writing a text of a thesis and abounds in useful linguistics assistance on its various parts. Feedback on students’ texts will constitute the major part of the course. The Course is offered in two modules which gradually build on the necessary writing and presentation skills. |
Anastasiia Sharapkova | 3 | ME030566l | ||
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First Steps to Thesis in English (Multidisciplinary)
Writing a Master's Thesis is a challenging job, so we encourage you to start working on it early enough to make a quality product. As you know, Skoltech requires a draft of the Literature Review and other chapters by January, which is only a few months away. Make the maximum of the time available and join the course 'First Steps to Thesis in English'!
The Course offers concise and practical guidelines for making the first steps in writing a Master's Thesis at Skoltech. Minimum theory. Maximum practice. Thorough and individualized feedback. Students will analyze authentic papers in their disciplines and learn to emulate the best examples. Participants will get insights into the structure, vocabulary, and grammar of each part of the thesis; they will edit and improve their text based on the instructor’s feedback. By the end of the course, a successful student will: – Develop linguistic awareness needed to avoid the typical pitfalls in writing and live presentations; |
Elizaveta Tikhomirova | 3 | ME030566 | ||
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Fundamentals of Energy Conversion Physics and Technology
Classical equilibrium thermodynamics is a theory of principles, which provides a framework to study means to produce motive power and useful heat, crucial for our everyday life. It is a pillar of any serious physics and engineering curriculum. This graduate course provides the students from possibly diverse backgrounds with the theoretical concepts that underlie the physics of energy conversion at the heart of heat engines operation, including chemical processes, and the specific knowledge of energy technologies in use nowadays.
Covering some of the main real-world technologies for the generation of electric/mechanic, heating and cooling power, students will learn to critically analyze and assess these technologies to improve their performance and imagine innovative and commercially viable solutions to energy problems, accounting for costs and environmental aspects like pollutants formation and their abatement. Essential notions which are taught include: energy conversion; heat transfer; work; first and second principles; working fluids and thermoelastic coefficients; thermodynamic cycles; motors and refrigerators. The course is organized around the learning of essential concepts and an awareness development of current energy technologies. While it is based both on "teaching with lecture" and "teaching with discussions" methods, report writing and oral presentations are a key element of the learning experience. In addition to home assignments and projects, students will solve problems during tutorials and discuss their solutions. |
Henni Ouerdane | 6 | MA060537 | ||
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Fundamentals of Photonics
This course is aimed for the first year master students, and provides an overview of the main physical principles of photonics and photonic devices. The emphasis is made on the demonstration that the light matter interaction in photonic devices can be modified by means of the modern technology. The course give the illustration how the basic physical laws help to get qualitative understanding of different branches of photonics such as light emission, transmission and detection. This introductory course is designed for both theoreticians and experimentalists.
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Nikolay Gippius | 6 | MA060160 | ||
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Fundamentals of Power Systems
This course covers power systems analysis & operations, including fundamentals (balanced three-phase power), steady-state analysis (power flow), state estimation, operation (optimal power flow), security (contingency analysis and security-constrained optimal power flow), distribution grid operation, power systems dynamics and control, basics of control theory and protection systems and challenges and trend of future power systems.
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Oleg Khamisov | 6 | MA060007 | ||
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Geometric Representation Theory (Term 1-2)
Geometric representation theory applies algebraic geometry to the problems of
representation theory. Some of the most famous problems of representation theory were solved on this way during the last 40 years. The list includes the Langlands reciprocity for the general linear groups over the functional fields, the Langlands-Shelstad fundamental Lemma, the proof of the Kazhdan-Lusztig conjectures; the computation of the characters of the finite groups of Lie type. We will study representations of the affine Hecke algebras using the geometry of affine Grassmannians (Satake isomorphism) and Steinberg varieties of triples (Deligne-Langlands conjecture). This is a course for master students knowing the basics of algebraic geometry, sheaf theory, homology and K-theory. |
Mikhail Finkelberg |
63 per term
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DA060271 | ||
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High Performance Python Lab
This course is devoted to learning how to use Python for High Performance Computing on different architectures – multi-core CPUs and general purpose GPUs.
The course is oriented on practical knowledge, where the students will get a hands-on experience with Python code profiling, modern Python frameworks, such as Python MultiProcessing, Numba, Cython, mpi4py, PyCuda and others. Wide range of problem sets from linear algebra, image processing, deep learning, physics and engineering makes this course interesting and suitable for all levels of students from all CREIs. Students will also get the possibility to work on modern supercomputers. |
Sergey Rykovanov | 3 | MA030367 | ||
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History and Philosophy of Science. Candidate Exam
The aim of this course is to give to Skoltech students and postgraduates basic information about the main stages of the development of science from its birth in Ancient Greece through the Middle Ages and the Renaissance to Modern Times and to the great scientific revolutions of the XX century. Every man of culture especially a future scientist should know the impact of such great thinkers as Plato, Aristotle, Thomas Aquinas, John Buridan, Nicholas of Cusa, Copernicus, Galileo, Descartes, Newton, Boscovich, Darwin, Mendel, Bohr and Einstein (omitting many other brilliant names, that would be spoken about in the frames of the course) to the development of a scientific picture of the universe. Also there will be discussed the main topics and notions of the philosophy of science: demarcation between science and humanities, Popper’s theory of falsification, Kuhn’s theory of scientific revolutions, philosophical ideas of Lakatos and Feyerabend.
The course will consist of 18 3-hour lectures and 6 examination sessions (3 hour each). The students are to submit 6 written essays in English on the following themes: 1. Ancient Greek and Roman Science; 2. Medieval and Renaissance Science; 3. Scientific Revolution of the XVII century; 4. Science in the XVIII and XIX century; 5. Science in the XX-XXI centuries; 6. Philosophy of Science. For the final exam the students should prepare 10-15 minutes oral presentation on the scientific problem they are currently working upon, or on some topic connected with history of science and/or philosophy. Lectures 1-3 are devoted to the Ancient Greek and Roman Science Lectures 4-6 are devoted to Medieval and Renaissance Science Lectures 7-9 are devoted to the Scientific Revolution of the XVII century Lectures 10-12 are devoted to Science in the XVIII and XIX century Lectures 13-15 are devoted to Science in the XX-XXI centuries Lectures 16-18 are devoted to the Philosophy of Science |
Ivan Lupandin | 6 | DG060026 | ||
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Industrial Robotics
Industrial robots offer substantial gains in manufacturing productivity, particularly when integrated into an automated system. A great range of capabilities commonly includes a wide variety of basic industrial tasks: material handling and palletizing, machine loading, parts assembly, welding, spray painting, and tool operation.
This practical-oriented course aims to familiarize graduate students with intelligent industrial robots and gain valuable hands-on skills through extensive training sessions and utilizing modern concepts and techniques of controlling robot manipulators and types of end effectors. Also, students will be trained to handle different automation tasks required by the needs of a particular industrial sector. |
Dmitriy Dzhurinskiy | 6 | MA060249 | ||
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Information and Coding Theory
The aim of the course is to explain basic ideas and results of information and coding theory, some of which has been used for rather long time in data science, in particular various entropy inequalities, and some emerged just very recently, for instance, usage of error-correcting codes for improvements of k-means method for clustering. The course is divided into two parts: introduction to information theory and elements of modern coding theory. In the first part, we consider the measure of information, mutual information, entropy, evaluation of channel capacity for single user and multi-user channels. In the second part, we consider foundations of coding theory such as block codes, linear codes, bounds on the code’s parameters and the most popular algebraic coding methods (Hamming, Reed-Muller, BCH and Reed-Solomon codes). Then we consider modern coding techniques, i.e. iterative decoding systems and graphical models to represent them. Iterative techniques have revolutionized the theory and practice of coding and have been applied in numerous communications standards. We discuss low-density parity-check (LDPC) codes, factor graphs and Sum-Product decoding algorithm.
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Alexey Frolov | 6 | MA060122 | ||
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Instrumental Analysis in Molecular Biology
During this course you will understand principles of main instrumental methods that are used today in molecular biology both in academia and industry. The aim of this course is to provide knowledge of modern methods for master and PhD students with minimal background in the field of molecular biology of Eukaryotes. As a result, students will understand general principles of the methods used in the biomedical research and development. By focusing on examples of the biomolecule purification and purity confirmation the idea of accurate studies will be explained. The course will provide a comprehensive summary of the major methods used nowadays in the field, except microscopy. Current trends will be reviewed, along with a discussion of methods application for common tasks. Some attention will be paid to miniaturization of analytical devices for the use as POC (point-of-care).
This course includes a practical part, but in 2021 we will exclude it due to COVID restrictions. Previously (and may be in the future) students were be able to conduct full molecular biology investigation started from murine liver samples. Students perform purification of RNA and proteins followed by UV-spectroscopy, RT-qPCR, and western blot analyses. As a result, students should explain of differences between the liver samples based on the RT-qPCR and western blot data. During practical part we discuss all important steps of each protocol. PhD students have to choose and compare several alternative approaches and modified protocols to optimize the results and make them more “publication-suitable”. |
Yury Kostyukevich | 3 | MA030588 | ||
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Introduction to Computer Vision
Computer Vision is one of the most rapidly evolving subfields of Data Science with many applications, e. g. in autonomous driving and healthcare, among others. This course is designed to provide a comprehensive systematic introduction to the field. We'll start with the recognition of some simple object elements such as corners and edges and then proceed to the detection of more complex local features. All major problem statements such as image classification, object detection and segmentation as well as the corresponding classical algorithms will be covered within the course. Finally, we'll briefly introduce convolutional networks and discuss key deep learning architectures for the same set of problems.
We'll extensively use Python and CV & image analysis libraries scikit-image and OpenCV during hands-ons and homeworks. The final grade will be calculated using the results of three homeworks (20% each) and the final project (40%). |
Sergey Zagoruyko | 3 | MA030348 | ||
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Introduction to Linux and Supercomputers
The course teaches you set of skills necessary for programming in the Linux environment. It prepares you for using the Supercomputer, such as the primary Skoltech computational resource called "Zhores" in your future or current research.
Almost all Supercomputers in the world are based on the Linux operating system and consist of a cluster of computers connected by high-speed network. Understanding of computer Architecture is part of the course and you will learn how computational needs of your research determine what you can expect from a Supercomputer. Linux is the primary part of the course. You will learn Linux concepts and acquire the skills to use the terminology. For this we discuss concepts in the class, you read the suggested book and write essays to practice reasoning around the Linux concepts. These skills are basis for your ability to isolate and correct problems when working with a Supercomputer. The course teaches you theory and practice of Linux to acquire skills for writing automation scripts to handle workload on a Supercomputer. This includes the usage of Virtualization containers that allow you to transfer the programming environment from one computer to another. Then, you learn how to automate the compilation, the maintenance of C-based programs and the usage of software modules to handle a variety of programming environments. One of most important topics of the course is understanding of parallel programming with OpenMP and MPI programming paradigms and usage of accelerators, such as the GPUs. On the parallel programming – concepts of the performance scaling, data hazards and understanding of the race conditions will guide the design of processing algorithms for your (future) research. The course is build on exercises which you do on your own and we discuss in the class. Attendance of the course counts towards the graduation, as well as doing the exercises and writing the essays to make sure you master the terminology of all concepts. |
Igor Zacharov | 3 | MA030366 | ||
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Introduction to Natural Language Processing
This course gives introduction to the field of Natural Language Processing (NLP). The course provides a panorama of various NLP tasks and applications such as part-of-speech tagging, named entity recognition, and word sense disambiguation.
The course is largely based on the classic textbook by Jurafsky&Martin, but also features material on graph-based models for NLP and data annotation / crowdsourcing for NLP. The course on "Deep Learning for Natural Language Processing" at Skoltech provides complementary material to this introductory course focusing more on modern neural models and approaches, e.g. Transformers, and less on the various applications and related topics, such as crowdsourcing, graph-based NLP, and dependency parsing. |
Alexander Panchenko | 3 | MA030555 | ||
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Introduction to Optical Biosensors
The Introduction to Optical Biosensors course gives students the basic concepts of biosensors with specific focus on optical biosensor technology. The course underlines the following important directions: 1) optical transduction approaches, such as luminescence, absorption, surface plasmon resonance, etc. that can be used for the detection and identification of chemical and biological species; 2) biological recognition elements that can be used for the development of biosensors and their interactions for the detection of analytes; 3) the applications of nanomaterial and nanotechnology for fabrication of optical biosensors. The course describes broad range of application of optical biosensors for the detection of various analytes.
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Alexey Yashchenok | 3 | MA030640 | ||
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Introduction to Programming for Biologists
Science is constantly changing. The more and more complicated analyses are needed for publication, not only for a theoretical study but also for experimental research. In the light of ongoing development of new high-throughput experimental techniques, the skill of data processing and analysis becomes inevitable for any researcher.
One of the most used tools to process data and make publication-quality plots is Python programming language. Python is simple, powerful, and extremely popular. Among programming languages, Python is the closest to natural languages making it easy to begin with. In the course “Introduction to Programming for Biologists” I will provide basic knowledge of Python in a simple form suitable for understanding by all students, especially by completely unfamiliar with programming. With many exercises, students will inevitably master their programming skills to the level appropriate for processing their own data and making basic-to-medium analyses. |
Dmitry Ivankov | 6 | MA060372 | ||
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Introduction to Quantum Groups (Term 1-2)
Quantum groups were introduced in the mid-80's and very quickly became one of the most important topics in mathematics and mathematical physics. They are still actively studied, and their knowledge is necessary for work in many areas.
The purpose of the course is an introduction to quantum groups. The content will be based on classic works of the 80's and early 90's, we will not get to the latest results. Initial knowledge about quantum groups is not assumed, but acquaintance with Lie algebras and Groups, Poisson brackets, and the first notions of category theory is assumed. |
Mikhail Bershtein |
63 per term
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MA060426 | ||
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Introduction to Solid State Physics
This course gives an introduction to solid state physics, one of the cornerstones on which a modern technologies are based. We will begin with the conventional classification of solids on the basis of binding forces, to be followed by crystal structure description and experimental techniques for crystal studies. From there we will discuss classical and quantum aspects of lattice dynamics and begin the study of the electronic structure of solids, including energy band formalism and carrier statistics in metals, semiconductors and insulators. Next we will consider basics of the atomic and electronic processes at crystal surfaces and interfaces, kinetic effects and scattering of the electrons. We will continue with the study of the optical processes in solids, including nonequilibrium carrier dynamics and photovoltaic effects and their applications in technology.
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Sergey Kosolobov | 6 | MA060027 | ||
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Materials Characterization Techniques
The purpose of this course is to familiarize students with the modern practically useful methods of materials characterization spanning various diffraction, microscopy, spectroscopy and electrochemistry techniques.
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Stanislav Fedotov | 6 | MA060529 | ||
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Materials Selection in Design
This course illustrates the need for a scientific and practical method of selection of appropriate materials in the design for industrial application and system engineering. It includes the review of the principles of materials science including materials classification, hierarchical structuring, related properties, and performance of different class of materials such as natural materials, metals, ceramics, plastics, cellular solids. Ashby’s material selection algorithm for rational selection of materials for specific applications will be taught here in comprehensive way – analysis of function, objectives and constraints, deducing of performance indices. All the concepts covered in lectures will be practiced by using a commercially available software known as CES EduPack to implement data intensive learning. Individual course projects are aimed to taste the CDIO approach in Materials Selection for a product that must meet the sustainability requirements.
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Alexey Salimon | 3 | MA030099 | ||
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Mathematical Methods in Engineering and Applied Science (Term 2-3)
The course introduces students to mathematical methods widely used in modern engineering and applied sciences. It consists of three main parts: 1) Methods of Applied Linear Algebra (solving linear systems, LU, QR, SVD, and other factorizations, Principal Component Analysis, iterative methods, FFT, least squares, pseudo-inverse, etc.); 2) Statistical Methods and Data Analysis (mean, variance, probability; moments, covariance, Gaussian processes; regression, gradient descent; neural networks, machine learning); and 3) Applied Differential Equations (linear and nonlinear ordinary differential equations, stability and bifurcations of solutions, linear and nonlinear partial differential equations, hyperbolicity, characteristics, dispersion, reaction-diffusion phenomena, pattern formation).
The course is introductory by nature, covering a wide range of topics and methods of modern interest in applications. Its theoretical content is informal in style and most of the concepts will be illustrated with problems from engineering, physics, chemistry, and biology using numerical computations in Matlab and Python. The three parts of the course are aimed at: part 1 – using the right language that is crucial for understanding many computational techniques used in engineering; part 2 – learning important tools of analysis of results obtained either by computation or in experiments; and part 3 – learning the nature of key mathematical models that form the foundation of engineering and applied sciences. |
Aslan Kasimov |
63 per term
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MA060352 | CANCELLED | |
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Methods of Experimental Physics (Practical Course) (Term 2-3)
Practicum assumes experimental training in a real research laboratory, including experiment preparation, measurements, and detailed data treatment. The students are proposed to make a choice of three experimental problems in each of two terms (five problems are poroposed for Term 2 in the program below), with indicating one of these three as their principle problem. For the principle problem, the search for scientific literature, the comparison with the previously published data, and seminar presentation of the data at the end of the term are obligatory. Similar program will be later announced for Term 3.
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Valery Ryazanov, Pavlos Lagoudakis |
63 per term
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MA060585 | CANCELLED | |
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Modeling of Multiphase Flows and Flow Assurance
The course is focused on modeling and analyzing a number of transport phenomena accompanying transport of multiphase flows and also on flow assurance problems.
In application to petroleum engineering, modeling is required to properly evaluate risks and identify hydrocarbon production strategy. The major topics, which will be considered: – oil/water flows, – emulsion formation, – turbulent drag reduction – wax and asphaltene deposition in pipelines – other relevant subjects Besides modeling, a number of problems related to practical aspects of flow assurance (practical oil chemistry) will be also considered. Practical modeling of inorganic salts deposition, wax and asphaltene deposition modeling under field conditions by using tNaigator. |
Dmitry Eskin | 6 | MA060591 | ||
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Modern Engineering Graphics for High TRL Development
This course discusses important principles of modern design and making engineering drawings. It studies integrated rules and guidelines for development, processing, and handling a design documentation, which is developed and applied at all stages of a product lifecycle (designing, running, maintenance, etc.). After the course, students will be able to apply the unified rules for development, processing, and handling drawings, providing its completeness, quality, and proper exploitation of products, implementation of the modern means and methods in designing, harmonization with corresponding international standards, minimization of the manufacturing costs and complexity, protection of a natural environment, customer’s life and health. This course will allow learning the basic requirements of high TRL designing and using them at practice via the modern CAD modeling software.
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Konstantin Makarenko | 3 | MA030605 | CANCELLED | |
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Modern Wireless Systems - 5G and Beyond
The course covers modern wireless systems, including advanced telecommunication technologies (5G/6G) which form the essential basis for next-generation communication deployment. At the same time the course gives an overview of the opportunities for the mobile communication industry created by cross-industry transformation and traditional business models changes.
During the course the students will consider the following questions: During the course the number of lab sessions will be held with the use of 2G/3G/4G/5G testbenches. As a result of the course, the students will be able to provide a solid understanding of modern wireless communication concepts that satisfy the current telecommunication market needs in enabling next-generation communication models and new services. |
Dmitry Lakontsev | 3 | MA030410 |
MOVEDmoved to AY 2024-2025
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Molecular Biology (Term 1B-2)
Molecular biology course is based on learning the principles of replication, recombination, DNA repair. Additionally, replication strategies of phages and viruses will be discussed. Mitosis and meiosis will be described in a context of DNA biosynthesis. Also, the principles of RNA biosynthesis, i.e. transcription and processing, as well as protein biosynthesis, i.e. translation, maturation and transport will be described.
The goal of the course is obtaining a comprehensive knowledge on the structure of DNA and processes of DNA replication, recombination and repair in bacteria and eukaryotes, as well as on replication of phages and viruses. Also, students will obtain a detailed knowledge on the processes of transcription, in bacteria and eukaryotes, on the regulation of transcription in bacteria and eukaryotes, on examples of complex networks of transcriptional regulation in bacteria and eukaryotes, on maturation of RNA in eukaryotes, on protein biosynthesis in bacteria and eukaryotes, on the transport of protein in bacteria and eukaryotes. Alongside with comprehensive knowledge on fundamentals of molecular biology, students will get a brief understanding of current problems in the field and modern methods for their solving. Students activities include: |
Petr Sergiev |
63 per term
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MA060034 | ||
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Molecular Neurobiology
The Molecular Neurobiology course gives students the basics of molecular organization and functional principles of the central nervous system. This is a theoretical course, describing the current vision of how the central nervous system works at the cellular, subcellular, and molecular levels. The course will also introduce current methods used to assess the functional organization of the nervous system at the molecular level, with a particular focus on studies of the human brain. The course will include both the textbook information, as well as recent findings not yet included in textbooks.
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Philipp Khaitovich | 3 | MA030397 | ||
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Molecular Neurobiology
The Molecular Neurobiology course gives students the basics of molecular organization and functional principles of the central nervous system. This is a theoretical course, describing the current vision of how the central nervous system works at the cellular, subcellular, and molecular levels. The course will also introduce current methods used to assess the functional organization of the nervous system at the molecular level, with a particular focus on studies of the human brain. The course will include both the textbook information, as well as recent findings not yet included in textbooks.
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Philipp Khaitovich | 6 | MA060397 | CANCELLED | |
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Nanooptics
The Nano-optics course is designed to help students develop an understanding of optical phenomena at the nanometer scale, i.e. near or beyond the diffraction limit of light. Typically, numerous subtopics and applications of nano-optics are scattered across various disciplines, such as field theory, solid state physics, spectroscopy, photonics, quantum optics etc. At the beginning of the course we formulate general principles and discuss a theoretical framework to study the light-matter interaction at nanoscale. Afterwards, we focus on the theory and applications of plasmon-polaritons, hybrid light-matter quasiparticles that can be used to reach subwavelength confinement of light. In the third part of the course we discuss integrated photonic circuits, where a confined light can be used for both computation and communication between electronic devices. Along with achievements of this modern field we discuss challenges, such as the size mismatch between electronic and optical components. Particular attention is given to various plasmonic upgrades of photonic circuits elements, that promise both miniaturization and improved information processing speed. After completing the course, students will gain a general understanding of the field with a particular focus on modern trends in plasmonics and integrated opto-electronics. In addition to that, students will be able to improve their analytical skills by solving various mathematical problems of nano-optics in a framework of the field theory.
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Ivan Pshenichnyuk | 3 | MA030153 | ||
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Network Science
The course is a broad interdisciplinary introduction to the theory of complex networks and their applications to industrial and scientific problems from various fields of science: biology, physics, economics, sociology, etc. Topics that will be covered in the course include basic concepts from graph theory, spectral theory of random networks, structuring and pattern formation in random networks (communities, core-shell, etc.), statistical data analysis on real networks, as well as various applications of Network Science for problems in bioinformatics and industry. Students will learn about ongoing research in this emerging field and ultimately apply the knowledge gained to conduct their own analysis of real-world network data of their choice in the final project.
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Kirill Polovnikov | 3 | MA030504 | ||
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Next Generation Sequencing – Experimental Protocols and Data Analysis
Next generation sequencing is a group of methods that allow simultaneous sequencing of many thousands of DNA fragments without their physical separation by cloning. In the last 15 years it revolutionized many fields of biology: genetics, evolutionary biology, microbiology, anthropology. It have also many practical applications, especially in biomedicine. However NGS and corresponding bioinformatics methods are sometimes used in a black box regime, without understanding its logic, the area of applicability and limitations. This course will provide a comprehensive survey of both experimental aspects of NGS and of bioinformatics analysis of the data. We will also discuss the current trends in the development of sequencing technologies and their applications. The practical part of the course will offer a hands-on experience of a genomic project, starting from the experiment design, followed by preparation of DNA and RNA samples, sequencing, de novo genome assembly, annotation and analysis of differential expression.
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Maria Logacheva | 3 | MA030396 | ||
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Numerical Linear Algebra
Numerical linear algebra forms the basis for all modern computational mathematics. It is not possible to develop new large scale algorithms and even use existing ones without knowing it.
In this course I will show, how numerical linear algebra methods and algorithms are used to solve practical problems. Matrix decompositions play the key role in numerical linear algebra. We will study different matrix decompositions in details: what are they, how to compute them efficiently and robustly, and most importantly, how they are applied to the solution of linear systems, eigenvalue problems and data analysis applications. For large-scale problems iterative methods will be described. I will try to highlight recent developments when it is relevant to the current lecture. This course should serve as a basis for other IT Skoltech courses. It will also serve as a first-time place where programming environment and infrastructure is introduced in a consistent manner. |
Ivan Oseledets | 6 | MA060024 | ||
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Numerical Methods in Engineering and Applied Science (Term 2-3)
The course is intended to provide the understanding and working knowledge of
numerical methods required for modeling and simulation of complex phenomena described by ordinary and partial differential equations. The course focuses on understanding fundamentals of numerical methods such as accuracy, stability, convergence, and consistency rather than learning how to use canned computer codes. The course involves a fair amount of first-hand experience with programing and solving real problems on computers. Although the solid knowledge of calculus, linear algebra, complex variables is essential, only basic understanding of the theory of ordinary and partial differential equations as governing equations for physical and engineering systems as well as basic programming skills are required. The following topics are discussed: interpolation, numerical differentiation, numerical integration, numerical solutions of ordinary differential equations, and numerical solution of partial differential equations. Students will have to complete computer projects, mid-term and final exams. |
Oleg Vasilyev |
63 per term
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DA060239 | ||
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Optimization Methods in Machine Learning
Most machine learning tasks are formally optimization problems.
The first part of the course is an introduction. We will study/repeat classical concepts of optimization (convexity, optimality conditions, etc.) and optimization methods (gradient descent, conditional gradient method, conjugate gradient method, Newton's method, quasi-Newton methods). The second part of the course is devoted to the stochastic gradient method and its different variations, which are used in various learning problems. In the third part of the course we will talk about modern state-of-the-art methods for learning problems. First of all, we will discuss adaptivity and methods with momentum. In the fourth part of the course, we will focus on various distributed formulations of the optimization problems from cluster training to the now popular federated learning. |
Alexander Gasnikov | 3 | MA030632 | ||
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Organic Geochemistry of Petroleum Systems
The course offers an introduction to the principles and methods of organic geochemistry as applied to conventional and unconventional hydrocarbon systems.
The course covers: the basic chemistry and biogeochemical processes involved in the generation and transformation of organic matter to different types of fossil fuels; the depositional environments that lead to the formation of effective source rocks; the use of individual organic compounds (biomarkers) and their isotopes for exploration and development of hydrocarbon deposits. Specific examples, from published literature, of applying biomarker and their stable isotope compositions in petroleum basin studies will be discussed and evaluated. Lectures are supplemented by two seminars, which will allow students to explore the role of organic matter chemical composition and the thermal history of a sedimentary basin on source rock hydrocarbon potential. Two laboratory sessions will give students hands on experience with analytical chemical procedures for organic matter analysis. A distinctive new feature of the course is the discussion of the latest analytical and instrumental techniques for molecular and stable isotope compound-specific characterization of biomarkers from source rocks and petroleum. |
Nikolai Pedentchouk | 3 | MA030466 |
MOVEDmoved to Term 4
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Path Integral: Stochastic Processes and Basics of Quantum Mechanics (Term 1-2)
One of the most powerful methods of modern theoretical physics is the method of functional integration or path integration. The foundations of this approach were developed by N. Wiener at the beginning of the 20th century, but it spread widely after R. Feynman, who applied this approach in quantum mechanics. At present, the functional integral has found its application in the theory of random processes, polymer physics, quantum and statistical mechanics, and even in financial mathematics. Despite the fact that in some cases its applicability has not yet been mathematically rigorous proven, this method makes it possible to obtain exact and approximate solutions of various interesting problems with surprising elegance. The course is devoted to the basics of this approach and its applications to the theory of random processes and quantum mechanics. In the first part of the course, using the example of stochastic differential equations, the main ideas of this approach will be described, as well as various methods for exact and approximate calculation of functional integrals. Further, within the framework of the course, the main ideas of quantum mechanics will be considered, and both the operator approach and the approach using functional integration will be considered. It will be demonstrated that, from the point of view of formalism, the description of random processes and the description of quantum mechanical systems are very similar. This will make it possible to make a number of interesting observations, such as, for example, the analogy between supersymmetric quantum mechanics and the diffusion of a particle in an external potential. In the final part of the course, depending on the interests of the audience, various applications of the functional integration method will be discussed, such as polymer physics, financial mathematics, etc.
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Andrey Semenov |
63 per term
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MA060542 | ||
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course describes the intended learning outcomes and how they are assessed.
The main bulk of the 81 hours of the course is spent in the actual courses in which the PhD-students do their TA-assignments. The assignments in the course itself include TA proposal and TA report and for cohorts 2022-2025 participation in training: Towards excellence in Teaching Assistantship”. Educational department should approve all assignments in Canvas in terms of content and formal requirements respectively. |
Dmitry Artamonov | 3 | DG030005 | ||
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Permafrost and Natural Hydrates
This course is about permafrost and natural hydrates. The course is devoted to the consideration of cryogenic-geological conditions of the northern oil and gas provinces of Russia and their influence on the construction and operation of production wells. The course includes permafrost characterization within the main oil and gas fields in the European North and Siberia, including the Arctic zone. The main cryogenic-geological processes occurring in the areas of permafrost propagation are considered. The description of gas and gas hydrate accumulations in permafrost is given. The conditions for the formation and existence of gas and gas hydrate accumulations in permafrost are analyzed. Zoning of the territories of oil and gas provinces on the complexity of geocryological conditions for the development of deposits is carried out. The characteristic of engineering and permafrost studies for the selection of construction sites for producing wells is given. Analyzed the complications arising from the construction and operation of wells in permafrost. Thermal and mechanical interaction of producing wells with permafrost is considered. The behavior of gas hydrate accumulations in permafrost zone during the development of gas and oil fields in the Arctic is analyzed. The impact of global climate change on the stability of wells and ground engineering structures of the oil and gas complex is assessed.
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Evgeny Chuvilin | 3 | MA030343 | ||
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Photonics Devices and Measurements
The course will introduce to the students modern photonic based devices and applications that greatly influence current hi-tech world.
Focus will be put on device working principles, underlying physics (original scientific discoveries and inventions, patenting), engineering realization, modern applications and markets, future development trends. The following areas are planned to be covered: – Modern telecommunications – networks, devices, components; The following topics will be covered for each considered device (or class of devices): A. Technical aspects (70% of the material): 1. Underlying original technology, scientific discovery, invention. B. Market & business aspects (30% of the material): 1. Overview of modern markets. We will also discuss common approaches to commercialization of deep-tech technologies. It will be demonstrated how general concepts and rules work in specific commercialization cases – taking examples from devices and technologies considered in the course. |
Pavel Dorozhkin | 3 | MA030582 | ||
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Photonics Research Seminar Series (Term 1B-4)
The course "Photonics Research Seminar Series" aims to provide MSc and PhDs with a broader perspective into state-of-the-art research in photonics, from fundamental insights (both theoretical and experimental) to engineering and applications. The course also aims to train students in how to structure scientific results, how to provide a coherent narrative and how to appropriately present it in the form of a strictly-timed oral presentation. The bulk of the course consists of a weekly one hour seminar on seminal topics of photonics, from guest speakers or Skoltech faculty, researchers and students.
The course is a prerequisite for the research thesis defense of projects of the following laboratories (and is as such compulsory only for the 2nd year MSc students of these laboratories): |
Pavlos Lagoudakis |
30.75 per term
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MA030553 | ||
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Planning Algorithms in Artificial Intelligence
Planning is the process of deciding which action to take in order to achieve some goals. This course will study planning and decision making under the scope of Artificial Intelligence, that is, we will mostly focus on algorithms that calculate the actions to take by an agent.
The course will cover a wide variety of planning problems, such as discrete planning, path planning, continuous planning, decision-making, planning under uncertainty, learning-based, etc. This diversity corresponds to the main objective of the course: provide a solid understanding to the student to successfully apply different planning techniques into a large variety of problems, including robotics, controls, manufacturing, drug design, computer graphics, and aerospace applications to name a few. The evaluation will consist of problem sets, related to seminar material prepared in class plus a final group project. |
Gonzalo Ferrer | 3 | MA030420 | ||
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PLM 3 - Digital Technologies in Verification and Validation of Complex Technical Products Design Solutions
This course is the final topic in the PLM course series and it is devoted to the manufacturing, assembly, simulation and testing of a prototype. The course encompasses different types of physical testing accompanied by their respective numerical models. Verification and validation for the technical system is performed.
The students learn how to perform vibrational and modal testing in order to identify dynamic parameters of a given structure. The modal testing is performed using laser scanning vibrometry. The results of modal and vibrational testing are used for finite-element model validation and updating it for an accurate dynamics simulation. The Hardware-in-the-Loop (HiL) testing is an important part of the course also. During HiL testing the functional model of the investigated system is uploaded to a real-time board called "Iron Bird" and it is tested in combination with physical parts. The students get hands-on experience on Simcenter, LabView, Matlab software and advanced physical testing equipment. In parallel with the specific testing and modeling activities described above, the students finalize the manufacturing and assembly of a prototype. A number of tests are performed on the fully assembled system. Students get experience with aerodynamic testing (lift, drag and thrust measurement) and final field testing of the assembled prototype. |
Andreas Panayi | 6 | MA060604 | MOVED FROM TERM 5 | |
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Power Markets and Regulations
The course will introduce the students to power system economics. After covering the fundamentals of microeconomics, the main types of electricity markets and regulations will be discussed including the Russian market. Economic dispatch and Optimal Power Flow with Locational Marginal Pricing will be covered. Capacity planning, ancillary services, and risk analysis are also covered.
The lectures will be supplemented by homeworks utilizing PowerWorld simulation package, a laboratory exercises investigating gaming in power markets and group mini-projects. |
Marina Dolmatova | 6 | MA060441 |
MOVEDmoved to Term 3
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Principles of Applied Statistics
Standard courses in mathematical statistics focus on classical statistical methods. However, in practice, modern statistical methods are often used, for example, bootstrap, nonparametric estimation, smoothing based on decomposition in orthogonal bases, methods for reducing dimensionality and sensitivity analysis, etc. Understanding the theory underlying these methods, as well as the ability to apply them in practice, is absolutely necessary for anyone working in mathematical statistics and data analysis.
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Maxim Panov | 3 | MA030416 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Nikolay Brilliantov | 3 | DD030020cd | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Mikhail Spasennykh | 3 | DD030020pe | ||
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Representations of Finite Groups (Term 1-2)
Representation theory is used in many areas of mathematics (algebra, topology, algebraic groups, Lie groups and Lie algebras, quantum groups, algebraic number theory, combinatorics, probability theory, …), as well as in mathematical physics. Therefore, mastering the basic technique of representation theory is necessary for mathematicians of various specialties. The aim of the course is to give an introduction to representation theory on the material of finite groups. Particular attention will be paid to representations of the symmetric groups.
Tentative program: 1. Reminder of the basics from the algebra course: group algebra of a finite group, irreducible representations, Schur's lemma, characters, orthogonality relations, Maschke's theorem, Burnside's theorem 2. Representations of finite Abelian groups, duality for finite Abelian groups, Fourier transform, biregular representation 3. Intertwining operators, induced representations, Frobenius duality 4. Mackey machine, projective representations, coverings over symmetric groups 5. Functional equation for characters, Gelfand pairs, spherical functions, connection with orthogonal polynomials 6. Representations of the symmetric group: various approaches to the classification and construction of irreducible representations 7. If time permits: principal series representations for the group GL(N) over a finite field, Hecke algebra, Harish-Chandra theory |
Grigori Olshanski |
63 per term
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MA060595 | ||
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Research Methodology: CMT Research Seminar (Term 2-4)
This is the main scientific seminar for the Skoltech Center for Materials Technologies (CMT). All MSc students either enrolled into the Master Program in Advanced Manufacturing Technologies or PhD students affiliated with CMT should attend this seminar. The format of the seminar is weekly invited lectures from top scientists in the research fields related to Advanced Manufacturing, Digital Engineering Technologies, and Mechanics and Physics of Advanced Manufacturing. The seminar is held in hybrid online/offline format.
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Ivan Sergeichev |
31 per term
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DG030102dm | ||
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Research Practice Seminar "Energy Systems and Technologies" (Term 2-4)
This research seminar is the general meeting for faculty, researchers and master and PhD students of Energy Systems programs. The seminar takes place every week during Terms 2(6)-3(7)-4(8).
Master students must attend the seminar at least for one academic year but welcome to attend during two years. PhD students are welcome to attend the seminar during all years of studies but can gain no more than 6 credits in total. The seminar consists of faculty lectures, invited lectures of top scientists in their research field as well as students’ reports on their own or examined papers. To PASS the course and gain 3 credits per academic year the student must fulfill all three requirements: 1. Attendance: > 2/3 of seminars. 2. Presentation. Depending on the status: 3. Active participation in the discussion. Being able to pose a question for every presentaion. The core of the self-study activity will be preparation to the talk that is comparable to project implementation (a significant part of many regular courses). The students are expected to assign at the beginning of Term 2/6 and may drop the seminar till the beginning of Term 3/7 while credits are provided in Term 4/8. |
Elena Gryazina |
31 per term
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MA030489 | ||
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Research Seminar "Advanced Materials Science" (Term 1B-4)
This is a research seminar of the Skoltech Center for Energy Science and Technology and Materials Science Education program featuring presentations of young Skoltech researchers (MSc students, PhD students, postdocs) as well as external invited speakers. Every MSc and PhD student should deliver at least one presentation during the course. The range of topics is broad and includes any aspects of materials science. As a rule, each speaker presents results of his/her own research with a particular focus on research methodology. Every presentation is followed by a discussion.
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Sergey Luchkin |
30.75 per term
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DG030302i | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, cluster algebras etc.
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Andrey Marshakov |
61.5 per term
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DG060268 | ||
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Reservoir Rock Characterization
The course provides conclusive theoretical knowledge in reservoir characterization and laboratory measurements of the rock properties. During the course the students will learn the key rock parameters, defining the reservoir geological and recoverable reserves, well design and completion, field development strategy. Particular emphasis is made on the genesis and relationships of the rock properties as well as their relevance for well logging data interpretation and geological modeling.
The course consists of lectures and seminars, including video demonstration of laboratory equipment, used for measuring rock properties. The participants will be acquainted with every stage of reservoir rock study, starting from coring while drilling, core handling at the well site, its further processing, sampling, transportation and preparation for laboratory tests. Further the students will learn in details routine and special laboratory tests (SCAL). Finally, the students will study how to design a laboratory program, acknowledging geology and rock properties of a reservoir. |
Alexei Tchistiakov | 3 | MA030346 | ||
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Robotics
Robotics became the key driving force for the new industry and hundreds of thousands of industrial robots are installed annually. Students of the course will learn how to design innovative multi-DOFs, mobile, and flying robots. They will learn how to program Arduino, STM32, ESP microcontrollers, digital filters, and to write the software for controlling the servo and DC motors. Topics will include Forward and Inverse kinematics of 6-DoF robots, Jacobians, Robot dynamics, Trajectory planning, PID control. Python for robots and Robot Operating System (ROS) Lectures will help to develop strong skills in robot programming. Students will be taught the unique robotic technologies, such as, inverse Delta robot DeltaTouch and touch-sensitive soft robotic gripper, both are designed in ISR Laboratory. Students will also have a chance to develop autonomous mobile robots for Eurobot International Robotics Competition. Students will have the access to world’s top-level robotic solutions, e.g. KUKA IIWA, Universal Robots, Fanuc Delta Robot, DJI drones. The invited speakers will be the leaders of startups founded by ISR Lab. students, such as, Native Robotics, Tsuru Robotics, Sizolution and PhD students from Italian Institute of Technology, University of Southern Denmark, Tohoku University, etc.
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Dzmitry Tsetserukou | 6 | MA060050 | ||
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Selected Topics in Energy: Physical, Chemical and Geophysical Challenges (Term 2-4)
The course provides an introduction to the modern topics related to fundamentals of exploration of energy resources, energy generation, storage, conversion and use. It identifies the corresponding practical challenges to be addressed at the fundamental research level and familiarizes the students with the state-of-the-art approaches, methods and techniques in use in related scientific areas. The course seeks to emphasize and maintain interdisciplinary nature of the energy-related topics, in particular, combination of micro- and macroscopic approaches of geophysics, mechanics and chemistry in hydrocarbon exploration and development, relation between the physical and chemical processes of energy generation and conversion, integration of physical, chemical and mechanical approaches to perspective materials (physical and chemical synthesis, micro- and macroscopic characterization, structure-property relations, etc.) and related theoretical methodologies. These interdisciplinary links are mostly demonstrated by horizontal knowledge exchange among the students reporting and discussing practical examples from their own research field or from modern review or research publications. Topical lectures are included for further exploration of these links. The secondary aim of the course is the development of presentation skills (oral and writing), as well as scientific peer-review experience. The seminar format chosen for most activities allows students free exchange of knowledge and ideas, broader vision of their research projects and methodologies, better assessments of their own research skills and demands for further education.
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Alexei Buchachenko |
62 per term
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DG060106 | CANCELLED | |
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Startup Workshop
Startup Workshop (SUW) is the 6-credit E&I course designed to accelerate the Skoltech student/faculty/researcher teams developed and inspired by the Innovation Workshop and similar project-based E&I courses and programs (SFW, TEF, STRIP etc). Still, any Skoltech team is welcome to join the SUW through the mechanism of competitive selection. SUW course is extremely practical and pragmatic: its whole and only point is the preparation of the project application for the startup grant financing coming from two core Russian entrepreneurial infrastructural organizations: Skolkovo Foundation (SkF) and FASIE (aka Bortnik).
Despite such formal learning objective may look too narrow and mundane, it is proven to be beneficial to the project teams in two unique ways: — 1) building well-developed and structured SkF/FASIE application is an intensive exercise, that requires major effort in CustDev and prototyping, thus providing both learning and development to the initial project. Team that passed the SUW will be ready to face each and every venture investor of the world; — 2) as SUW teams enter the formal path of Skoltech Startup Funnel, they will obtain an intensive help from the Skoltech Dept of Business Devt, that will provide not only mentoring, but also some minor competitive financing, as well as E&I scholarship. SUW pushes teams through the preparation of the SkF/FASIE application that consists of 6 building blocks: 1) problem statement & validation, 2) solution & prototype description & validation, 3) competitive analysis and market assessment, 4) commercialization plan, 5) team and roles, 6) integrative 3-yr plan. Please note that SUW class is quite intensive: it starts well before the Term 2 with the competitive selection and requires serious work each week to produce the graded submission. |
Dmitry Kulish | 6 | E&I | MC060025 | |
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Stochastic Methods in Mathematical Modelling
Stochastic processes play an important role in natural sciences, computational theory as well as in sampling and synthetic data generation for machine learning. The course aims to cover basic methods of stochastic modelling, such as: Monte-Carlo methods, the modelling of scale-free phenomena as well as stochastic optimisation approaches.
The first part of the course provides an introduction to the methods of description and generation of randomness. The main idea is to establish a firm ground for more advanced topics and to help students feel comfortable with advanced machine learning courses. A special emphasis is put on the ubiquitous scale-free and non-gaussian stochastic processes also known as anomalous diffusion. Different causes of these processes will be discussed with examples such as practically important class of first-passage problems. The second half of the course will deal with Monte-Carlo algorithms, inference and learning, classical random network theory, Markov decision processes and stochastic optimal control. |
Vladimir Palyulin | 6 | MA060363 | ||
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Structural Analysis and Design
The Structural analysis and Design course gives students basics of strength analysis and design of structural members. Theoretical sections include introductions into stress and strain theories, failure criteria, elasticity theory, nonlinear material behavior and analysis of tension, compression, bending and shear structural members. The students work with Abaqus software to develop simple finite element models validated by analytical solutions. Typical structural design problems are considered in the framework of course projects. Students welcome to propose the problem statements for the course projects in order to support their MS or PhD projects.
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Ivan Sergeichev | 6 | MA060067 | ||
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Sustainability and Resilience of Energy System
The course is designed to help students understand key strategic challenges that are facing energy companies and influencing their investment and risk management decisions. Students will discuss technological sanctions, re-orientation toward new export markets, climate change and climate regulation, technological innovation. During the course, students will assess impact of climate change on their company/ country, play a climate negotiation game, develop recommendation for strategy update of an energy company.
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Irina Gayda | 3 | MA030572 | ||
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Symplectic Geometry (Term 1-2)
Symplectic geometry is a kind of skew-symmetric analogue of Riemannian geometry. This domain of differential geometry serves as a geometric basis of calculus of variations, quantum and classical mechanics, geometric optics and thermodynamics. The language of symplectic geometry is used everywhere in modern mathematics: in the Lie group theory, the theory of differential equations, integrable systems, singularity theory, topology. The course will cover both basic concepts of the theory and some specific problems needed for particular applications.
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Maxim Kazarian |
63 per term
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MA060596 | ||
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Systems Engineering
The course introduces students to the fundamentals of systems engineering as an interdisciplinary approach and means to enable the realization of successful systems, as defined by the International Council of Systems Engineering.
The course covers the entire spectrum of the lifecycle management of a system, encompassing conceptual design, design, implementation, assembly-Integration and test (AIT), operations and disposal of systems. Being a foundational course for the Space and Engineering Systems students of Skoltech, the course discusses many applications of systems engineering including some parts of space systems engineering . The course also discusses systems architecture principles. The Systems Engineering course follows the systems engineering V-model as an educational guideline. The course includes a design project that is conducted throughout the term. |
Clement Fortin | 6 | MA060023 | ||
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Technology Entrepreneurship Seminar: Foundation (Term 1B-2)
The course is designed to help you to learn and master practical skills of technology entrepreneurship and to accelerate your startup projects up to “external support/funding ready” level. It is intended for students: (1) interested in new tech venture creation and technology entrepreneurship; (2) having their own projects/ideas in development; (3) planning startup contest participation, pitch to investors, applying to Triple Point Contest, FASIE (“Bortnik Fund”), STRIP, accelerator/incubator program, etc. The startup project concept may be in the early experimental mode, or further along in its evolution such as seeking customers or pilot tests.
The course is conducted as practical and hands-on lab. We will not study entrepreneurship as a theoretical subject through external cases or papers. The material for learning comes directly from the class projects and the issues faced by students in converting their ideas into successful ventures. This assures projects real development and creates a highly dynamic environment for teaching where the faculty is a facilitator, mentor, tracker, and lecturer at the same time. The course subject areas represent “golden standard” for tech startups: (1) Problem and Market Need; (2) Product and Technology; (3) Market opportunity evaluation; (4) Business Model; (5) Startup Team; (6) R&D/Marketing/Sales/Funding plan; (7) Storytelling and Pitching. Upon the course completion, you and your team will The teams interested in the further development of their projects can continue seamlessly by entering the course "Technology Entrepreneurship Seminar: Advanced" (E&I track, 3 credits, Terms 3 & 4). |
Alexey Nikolaev |
31.5 per term
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E&I | MC030029a | |
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Theoretical Methods of Deep Learning
Deep Learning (DL) is an extremely important applied science that, at present, is poorly understood theoretically. We know that neural networks work well, but cannot fully explain why. Nevertheless, in recent years, there has been a rapid growth of publications that shed light on the new mathematics underlying DL, and we see now many interesting connections between DL and various other fields, e.g. approximation theory, differential equations, information theory, random matrix theory, statistical physics. This course aims to introduce students to these cutting-edge developments.
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Dmitry Yarotsky | 3 | MA030327 | ||
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Thermal Petrophysics and Geothermy
The course presents theoretical and experimental background of modern thermal petrophysics and geothermics in application to solution of actual problems of fundamental and applied geophysics and geology of unconventional hydrocarbon resources. The recent essential evolution in experimental and theoretical basis of the thermal petrophysics and oil&gas geothermics is described. Qualitatively new possibilities in prospecting, exploration and development of unconventional hydrocarbon fields based on advanced theoretical and experimental basis of thermal petrophysics and geothermics are shown. Important peculiarities in applications of new methods of thermal petrophysics and geothermics to heavy oil fields and shale oil fields are illustrated. Necessity of wide implementation of new technologies of thermal petrophysics and geothermics are demonstrated on examples of their applications for investigations of many reservoirs with unconventional resources of hydrocarbons. It is shown that cardinal changes in effectiveness of thermal petrophysics and geothermics are revealed due to development of optical scanning and continuous thermal core logging technologies. Significant improvements of reliability of basin and petroleum system modeling as well as hydrodynamic modeling are demonstrated as new methods of thermal petrophysics and geothermics became obligatory components of prospecting, exploration and development of unconventional resource fields.
As a result, students will know a modern basis and highly effective methods and equipment of thermal petrophysics and geothermics to apply the new techniques for prospecting, exploration and development of unconventional hydrocarbon fields. |
Yuri Popov | 6 | DA060295 | ||
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 | ||
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Transgenic Models in Drug Discovery
The course consists of theoretical and practic parts.Theoreticla part is devoted to analysis of transgenesis in C.elegance, Drosophila, Zebra fish and mice with particular accent on the usage of these platforms in drug development. It also describes general principles of creation of genetically modified animals. Practical part enables students to obtain practical skills in all phases of the production of transgenic mice.
Transgenic animals are no alternative tool for studying gene function, modeling of human diseases, creating of animal-producing recombinant proteins for agricultural and pharmaceutical industries. Last years work on the creation of such organisms was intensified due to the widespread introduction of site-specific nucleases technology: "zinc fingers», TALEN, CRISPR / Cas9 types of nuclease. Increasingly, it can be heard about the creation of new models of diseases, the use of gene knockout for medical purposes. |
Yuri Kotelevtsev | 6 | MA060398 | CANCELLED | |
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Transgenic Models in Drug Discovery
The course consists of theoretical and practic parts.Theoreticla part is devoted to analysis of transgenesis in C.elegance, Drosophila, Zebra fish and mice with particular accent on the usage of these platforms in drug development. It also describes general principles of creation of genetically modified animals. Practical part enables students to obtain practical skills in all phases of the production of transgenic mice.
Transgenic animals are no alternative tool for studying gene function, modeling of human diseases, creating of animal-producing recombinant proteins for agricultural and pharmaceutical industries. Last years work on the creation of such organisms was intensified due to the widespread introduction of site-specific nucleases technology: "zinc fingers», TALEN, CRISPR / Cas9 types of nuclease. Increasingly, it can be heard about the creation of new models of diseases, the use of gene knockout for medical purposes. |
Yuri Kotelevtsev | 3 | MA030398 | CANCELLED |
Winter
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course describes the intended learning outcomes and how they are assessed.
The main bulk of the 81 hours of the course is spent in the actual courses in which the PhD-students do their TA-assignments. The assignments in the course itself include TA proposal and TA report and for cohorts 2022-2025 participation in training: Towards excellence in Teaching Assistantship”. Educational department should approve all assignments in Canvas in terms of content and formal requirements respectively. |
Dmitry Artamonov | 3 | DG030005 | ||
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Teaching Essentials
Teaching essentials (self-paced) is a "Teaching essentials" course organised in a self-paced online manner instead of a series of workshops. The course aims to prepare Independent Study Period (ISP) student-instructors in course design and classroom delivery, help them navigate ISP courses, establish peer support among ISP student-instructors, and expand the teaching toolkit to create meaningful learning experiences. In addition, this initiative aims to support ISP student-instructors before and during the teaching period. Indeed, teaching for the first time can be quite a challenge and a complex multi-stage endeavour. That's why this short course is offered to all MSc and PhD students involved in teaching.
This course is: The self-paced course consists of the following: |
Alexander Vaniev | 1 | Extra | MF000005 |
| Course Title | Lead Instructors | Hours | Course Code |
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| Research/E&I project | 40 | I-2022-39 |
Term 3
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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Academic Communication: Preparatory English for PhD Exam (Term 3-4)
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities to be performed in class and at home, individually and in teams. By the end of the course, successful participants will |
Elizaveta Tikhomirova |
31.5 per term
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Extra | DF030029 | |
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Academic Writing Essentials
Academic writing skills are necessary for effective research, innovation, and educational activities in a multinational setting. The aim of the course is to provide guidelines and strategies for writing academic texts, focusing on relevant aspects of grammar, vocabulary, and style. The course includes analysis and practice of various forms of scientific and technical writing, and builds writing skills from sentences to paragraph structure, from summary to abstract, and lays the foundations for writing scientific papers and Master Thesis.
Modern science is, for most purposes, a collective collaborative effort, so the course is designed to promote individual and group responsibility by providing mutually related and time-dependent tasks, such as peer review. The course is writing-intensive with ample opportunity to practice editing and peer-reviewing. |
Elizaveta Tikhomirova | 3 | ME030569 | ||
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Advanced Quantum Mechanics
Lecture Course “Advanced Quantum Mechanics” comprises a number of topics which are not included in standard courses on Quantum Mechanics. Meanwhile, these topics acquire increasing importance during last 2-3 decades due to developing applications in various branches of quantum condensed-matter physics theory. The first set of topics refers to examples of adiabatic or weakly non-adiabatic behavior of quantum system: Berry phases and Landau-Zener tunneling. Secondly, we discuss Feynman path integral representation of quantum mechanics. The third part of the course is devoted to the density matrix formalism for description of open systems.
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Aleksandr Korotkevich | 3 | DA030207 | ||
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Applied Geomechanics
This course covers various aspects of experimental geomechanics and consists of three parts: lectures, seminars and laboratory work in Skoltech Center for Petroleum Science and Engineering (SCPSE). The lectures provide an introduction to the basic principles of measuring the physical characteristics of rocks during geomechanical testing in the laboratory. Seminars demonstrate examples of geomechanical parameter calculations based on the results of laboratory testing using MS Excel. Usually, oil and gas companies collect rock cores while drilling wells and then send them to various laboratories to determine the physical characteristics of the rocks under in situ reservoir conditions through comprehensive geomechanical testing. These rock parameters should be used as the basis for creating realistic modeling of reservoirs, so the results of laboratory testing is extremely important for the proper design and development of the most efficient strategy of hydrocarbon recovery in the field conditions.
The final part of the course will take place in the SCPSE laboratory. We will demonstrate to students the conventional geomechanical testing by loading rock samples up to their failure under confining pressure, simulating underground conditions, as well as hydraulic fracturing of the rock samples by injecting a high-pressure fluid into the rock. Students will get the results of the rock tests for their homework, they will have to calculate the rock parameters, and then present their results during the project defense. Students are considered to have passed the final exams of Applied Geomechanics course, if they demonstrate their ability to (i) understand the basics of various measurements in the laboratory; (ii) make calculations of rock parameters based on laboratory data; (iii) evaluate the accuracy of rock parameters measurements in the laboratory. |
Sergey Stanchits | 6 | DA060190 | ||
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Basic Molecular Biology Techniques
The classes take place in the operating molecular biology laboratory and include practical experimental work along with lectures covering the theory of molecular biology methods used through the course. The purpose of this course is to provide students with the opportunity to obtain and develop the basic set of skills required for the work in a molbiol lab. We see it as important to give a comprehension about how molbiol methods work, what are their mechanism, scope and features of application. During the course, students will perform molecular cloning, point mutagenesis and gene knockout in E. coli cells. The course is adapted for students with varying levels of lab experience. A mandatory requirement for joining the classes is a successful passing the lecture course in molecular biology.
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Alexey Kulikovsky | 6 | MA060022a | ||
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Biomedical Mass Spectrometry
This course introduces students to the first principles and methods of mass spectrometry with special emphasize on biological and medical applications. The course will cover wide range of mass spectrometry techniques used for ion generation, separation, detection and data processing and interpretation. The course will teach the theoretical fundamentals required for the design of instruments and methods for measuring mass spectra of biological samples. The course will cover mass spectrometry applications in OMICs technologies, in biomarker discovery and in tissue imaging.
After successful completion of this class, students will acquire the initial knowledge of the operational principles and design of different mass spectrometers, different methods of ionization of biological molecules of wide mass range, different methods of ion separation including magnetic sector, time of flight, RF and DC ion traps, as well as FTICR. Experimental and bioinformatics-based methods of protein, peptides, lipids and metabolite molecule identification, different fragmentation methods for primary and secondary structure determination, methods of quantitative determination of proteins, lipids, metabolites and small molecule in physiological liquids. |
Evgeny Nikolaev | 3 | MA030256 | CANCELLED | |
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Biostatistics
This introductory course to statistics and probability theory is modeled as an extension of a traditional university Statistics course and Advanced Placement Course in Statistics to a broader spectrum of topics, while keeping the spirit of quantitative discourse applied to real-life problems. The material is offered in 5 consecutive modules (see Course Outline below), each containing a lecture, a discussion section, and a practicum. For practical exercises we use R programming language and R-Studio software. However, this course is focused on statistics rather than R; therefore, each practicum is designed with the purpose to demonstrate and reinforce understanding of concepts introduced in the lecture rather than to provide a training in R.
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Dmitri Pervouchine | 6 | MA060036 |
MOVEDmoved to Term 4
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| Blockchain E&I | Dmitry Kulish | 6 | E&I | MC060639 | |
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Business Communication
Business Communication is an intensive hands on, practical course, designed to provide Skoltech students with the set of skills needed to effectively communicate with others – their classmates, working teams, professors and any audiences inside and outside of Skoltech. The course learning outcomes correspond directly with the Group 3 of Skoltech learning outcomes – “Relating to Others – Communication and Collaboration”. The course will show students the secrets and technologies to becoming confident when speaking in public – developing the skills they will be able to use throughout their career and their life. In a highly interactive, informative and supportive manner through in-class activities, games and simulations the course will enable students to: Speak with confidence and overcome their nervousness; Establish rapport with any audience; Present their message in a clear, concise, and engaging manner; Successfully manage impression they make onto audience; Create—and repurpose—presentations quickly and efficiently; Make successful and memorable pitch; Sharpen the story they want to tell; Use confidently body language and movement, strengthening their speech; Respond to questions and comments without getting flustered; Gain people’s attention, respect, and cooperation.
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Maxim Kiselev | 3 | E&I | MC030014 | |
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Cancer Biology
The course is dedicated to basics of clinical and molecular cancer biology with emphasis on innovative drugs and technologies for cancer treatment and diagnostics.
The main themes to be discussed: What is cancer? Classification, staging, grading, еhe hallmarks of cancer; Mutations, oncogenes, tumor suppressors, пenomic instability, epigenetics in cancer initiation and progression (miRNA, siRNA, lncRNA, methylation, histones modification, etc.); Cell cycle and growth and it’s deregulation in cancer; Tumor energy metabolism (the Warburg effect); Cell death and it’s deregulation in cancer (necrosis, apoptosis, autophagy); Cell differentiation and dedifferentiation in cancer, EMT, Metastasis; Cancer stem cells hypothesis; Tumor stroma and heterogeneity, neoangiogenesis; The “seed and soil hypothesis” – pre-metastatic niche; Extracellular vesicles for communication between cells; Cancer diagnostics, tumor markers; Liquid biopsy; Biological factors in Cancer (inflammation, viruses, bacteria, microbiome); Target therapy, Immune checkpoint inhibitors; Emerging therapeutic modalities, Car-T, dendritic cells, viruses; |
Vera Rybko | 3 | MA030088 | ||
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Carbon Nanomaterials
The course covers the subject of carbon nanomaterials (fullerenes, nanodiamond, nanotubes, and graphene). The history of carbon compounds since antiquity till our days starting from charcoal to carbon nanotubes and graphene will be reviewed. The students will have opportunity to synthesize carbon nanotubes, graphene and nanodiamond (by CVD methods), to observe the produced carbon nanomaterials in transmission (TEM) and scanning (SEM) electron microscopes as well as by atomic force (AFM) microscope and to their study optical and electrical properties.
Totally 32 hours of lectures, 12 hours of exercises and 4 hours of discussion work. During the courses each student is supposed to give a short presentation (15 min) on a selected topic, to write an essay on other selected topic and to prepare an exercise report. |
Albert Nasibulin | 6 | MA060044 | ||
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Computational Materials Science Seminar (Term 3-4)
This is the main research seminar at Skoltech for Computational Materials scientists. All students of Computational Materials Science subtrack of Materials Science MSc program and Materials Science and Engineering PhD program should attend this seminar. Topics include materials modeling (at atomistic scale), theoretical and computational chemistry, theoretical and computational physics of materials, underlying mathematical methods and algorithms etc. Invited lectures are top scientists in their research field. Students are welcome to present their own research results at this seminar and expected to do this at least once per two terms.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/ |
Dmitry Aksenov |
30.5 per term
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MA030430 | ||
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Computational Methods in Plant and Animal Genetics
Quantitative genetic variation is the substrate for phenotypic evolution in natural populations and for selective breeding of crop and animal species. It also underlies susceptibility to diseases and behavioural disorders in humans, as well as responses to pharmacological therapies. As such, a thorough understanding of the underlying genetic control of these traits can help alleviate complex diseases and develop new and more personalized therapeutic interventions to improve human health. The genetics framework is that such characters are controlled by many genes and non-genetic environmental factors may also influence the trait.
Given the wealth of genomics data, the challenge is to get the most of observations. Large amounts of genomic data allow unprecedented opportunities to map the genotypes and the traits. This raises the question of the suitable computational methods to implement. A corner-stone is the Linear Model. The matrix form of the Mixed Linear Model is an unprecedented and successful tool to decipher the phenotypes – genotypes relationships. This is due to its extraordinary flexibility and the inclusion of genetic relationships among individuals into the models. The course is proposed as a skill-based learning approach based on analysis of real data sets and case studies in a variety of biological questions. Practices within team projects are presented to the class. Individual reports using reproducible research framework are also realized. |
Laurent Gentzbittel | 6 | MA060022c | ||
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Continuum Mechanics (Term 2-3)
Continuum mechanics is a section of mechanics and theoretical physics, or rather the continuation of theoretical mechanics that deals with analysis of deformable bodies. However, mathematics in continuum mechanics represents the main constructive tool. Continuum mechanics allows to demonstrate the power of logic and mathematical thinking. Based on a few fundamental postulates and principles, using the mathematical apparatus can reveal non-trivial, and even striking results.
Foundation of continuum mechanics consists of: This course uses tensor representations in the Cartesian coordinate system of the observer. But it will one shown in detail how to to write the continuum mechanics equations in the arbitrary curvilinear coordinate system. This way the common link is not lost and the exposition becomes easier and clearer. |
Robert Nigmatulin |
63 per term
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DA060181 | ||
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Control Systems Engineering
The control systems is the key part of such advanced technologies as self-driving cars, smart robots, UAV. Students of the course will learn how to make to control system to ensure desirable dynamic properties (overshoot, settling time, stability), analyze time and frequency response of LTI and non-linear systems, and build a mathematical representation of the physical systems with state space representation. They will develop the traditional (PID, force control), optimal control with LQR, and advance control systems, including Impedance control proposed by MIT Professor N. Hogan and MPC (Model Predictive Control). Lectures will also cover the important methods of navigation, localization and path planning of the autonomous robots, such as Simultaneous Localization and Mapping (SLAM), Artificial Potential Field (APF), Occupancy grid, Iterative Closest Point (ICP). Introduction to Deep Learning for Robotics and scientific programming with Python will provide valuable skills for high-level development.
The practice will include work with advanced mobile robots (HermesBot, UltraBot), quadruped robot (HyperDog), collaborative robots (UR3, UR10), and swarm of drones (SwarmTouch), motion capturing system VICON with submillimeter accuracy. Students will be acquainted with data processing of such sensors as RGB-D cameras of Intel RealSense, ImagineSource Global Shutter cameras, Velodyne LIDARs (16, 32, 64 laser beams), 6 DoF Force/Toque sensor, high-density tactile sensor arrays of UEC. At the end of the Lecture, the students will have the chance to write the scientific papers prestigious conference in the field, i.e. IEEE ICRA, IEEE IROS, IEEE SMC, IEEE CASE. |
Dzmitry Tsetserukou | 6 | MA060083 | ||
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Crystal Structure Investigation Methods
The course teaches theoretical and practical fundamentals of diffraction and electron microscopy methods applied to the analysis of the crystal structure, nano- and microstructure of materials. The course delivers basic knowledge on the theory of crystal structure analysis with various kinds of radiation, modern techniques of crystal structure determination, the analysis of the local structure of matter, defects and microstructure, theory of image formation in the electron microscope and a review on modern spectroscopic techniques with atomic resolution. The competences acquired in this course can be further used in all branches of material science dealing with crystalline matter. The course consists of lectures, seminars/practical lessons, laboratory works and exam.
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Artem Abakumov | 6 | MA060531 | ||
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Engineering Design Factory - Part II
This project based hands-on course is aiming to allow students to develop a concrete product for the market; they will learn and experience a detailed product development cycle including its modelling, industrial design, prototyping and testing in both the virtual and physical realities. The course comprises a few lectures on the advanced phases of product development methodologies and on the integration of the various dimensions to successfully achieve a product or system demonstrator with a credible market value. Teams of 4 or 5 students will continue the development of a previous project, aiming to reach a TRL level of 4 or 5. Students teams will work closely with industrial designers, develop a detailed solution with advanced product analysis tools and take into account the manufacturing principles and the market potential of the product. The manufacturing of a prototype which can be tested in a working environment will be an important goal of this project. The teams will present their product to external experts and stakeholders in a product fair; products with demonstrated market potential will be promoted by Skoltech to seek external funding.
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Clement Fortin | 6 | MA060533 | ||
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English. Candidate Examination
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research structured according to the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
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Entrepreneurial Marketing and Commercialization
Students will learn to find customers, create brands and build a good reputation for their startup, commercialize their projects and inventions through different sales channels, and build and develop enduring relationships with their customers.
Course participants are also prepared to build criteria for evaluating commercialization alternatives, gathering data and analyzing market information, defining brand identity, strategy, and the necessary entrepreneurial skills needed to develop brand's value and manage a viable business. |
Alexander Chekanov | 3 | E&I | MC030445 | |
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Finite Element Analysis
The course is intended to give basic knowledge and skills for finite element analysis method and modelling. The course introduces basics of FE theory and fundamentals. Static stress analysis, structural stability, crack propagation analysis, extended finite element method (X-FEM), explicit dynamic analysis, heat transfer and thermal-stress analysis and sub-modeling are discussed and experienced within modelling and solution of engineering problems. Application of constitutive material models and equations of state are сonsidered. Abaqus FEA software is used for development of models, analysis and post-processing. MS and PhD projects are supported in the framework of the course via individual course projects. Therefore, students are wellcome to participate for the problem statement for the course projects.
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Ivan Sergeichev | 6 | MA060355 | ||
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Foundations of Multiscale Modeling: Kinetics
The course is devoted to fundamental principles of modelling of kinetic processes at different time and space scales. The basic concepts are introduced, along with the theoretical and numerical techniques, which application to practical problems is illustrated. The course starts with the description of molecular kinetics in fluids, colloidal and polymer solutions; applications for molecular machines and nano-robotics is considered. Langevin and Fokker-Planck equations are introduced, supplemented with the theoretical and numerical tools of their solution.
Next, the Boltzmann kinetic equation is analyzed. An application of the fundamental theoretical and numerical techniques, such as Grad and Chapman-Enskog methods, Lattice Boltzmann and Direct Simulation Monte Carlo is illustrated. Derivation of transport coefficients and hydrodynamics equations, including these for dissipative fluids, is given. Green-Kubo relations as an alternative method for practical computation of transport coefficients is presented and compared with other methods. Based on the Boltzmann equation, the theory of aggregation-fragmentation kinetics is developed, leading to the generalized Smoluchowski equations. The basic concepts like Scaling, Generation functions, etc. are introduced for theoretical analysis and Gillespe algorithm and fast solvers for practical computations. In the rest of the course the above theoretical and numerical techniques are illustrated for the following applications: Surface growth, Phase transition kinetics, Random sequential adsorption, Nucleation & Growth and Nano-tribology. Theory of Active Matter, Traffic Models, Socio-dynamics and Complex Networks dynamics are also considered. The knowledge of undergraduate mathematics – the basics of calculus, linear algebra and probability theory, as well as reasonable skills in Matlab and Python are needed. A familiarity with the basic open source software is desirable. |
Nikolay Brilliantov | 6 | MA060326 | ||
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Fundamentals of Additive Technologies
Additive manufacturing (AM), also called 3D printing, has become an extremely promising technology of next Industry 4.0 nowadays. Unlike traditional manufacturing processes such as welding, milling and melting that involve multi-stage processing and treatments, AM allows to create products with new level of performance and shapes.
Moreover, this approach allows to produce prototypes and functional parts rapidly and leads to reducing costs and risks. Another crucial advantage of the additive technologies (AT) is the unprecedented design flexibility that let us create the samples of high quality based on different materials such as metals, alloys, ceramics, polymers, composite materials etc. The main goal of this course is to represent the fundamental basis of different AT to the students. In this course, a wide range of questions will be addressed, beginning from the process of chain and designing the structures up to various 3D printing technologies, materials and process parameters, benefits and drawbacks of AM approaches will be considered. During laboratory class we will get acquainted with the AT on various printing machines. Students will be able to create their own models, print them in metals, ceramics and polymers, and also analyze the properties of the final samples. During this course a complete cycle of production of samples using various 3D printing techniques will be explored both theoretically and practically. |
Igor Shishkovsky | 6 | MA060243 | ||
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Fundamentals of Metallurgy
The course "Fundamentals of Metallurgy" is part of the field of materials science, which focuses on metals and alloys. This course provides a fundamental understanding of the properties of metals and alloys. During the course, brief introductions to crystallography, phase diagrams and transitions, dislocations, material mechanical behavior, and the principles of metal heat treatment will be provided. Particular attention will be paid to the different methods of obtaining materials and the microstructure of materials. Lecture materials will be reinforced through practical exercises.
The goal of the course is to provide an understanding of the properties of metals and alloys in terms of phase compositions and transformations, structure, mechanical properties, and how these properties can be evaluated. The course is recommended for students who do not have experience in materials science but would like to conduct research related to metals or alloys. The course will also be helpful for those considering taking courses in additive manufacturing and structural analysis and design. |
Stanislav Evlashin | 6 | MA060519 | ||
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Fundamentals of Remote Sensing
This course introduces students to the first principles and methods of the observation of Earth surface, monitoring of Earth atmosphere and detection of different kind of radiation coming from Space. The course will cover wide range of the satellites-, aircraft-, rockets- and balloon- based techniques designed for environmental monitoring, meteorology, map making etc. Goals of the course include: a comprehensive knowledge of the principles and approaches to the creation and operation of remote sensing systems; acquisition of analysis skills of modern ERS programs; practical application of acquired knowledge and
skills for SWOT-analysis of complex information systems. Course will also include a module on geomatics, i.e. platforms, sensors and methodologies related to the collection, processing, analysis and interpretation of (2D/3D) data related to Earth's surface. This includes platforms like satellite or drones, sensors like LiDAR or airborne cameras and techniques like photogrammetry, laser scanning, geodesy, topography, etc. Major learning outcomes include operational principles and design of different sensors used in remote sensing of the Earth, practical skills to design an experiment in remote sensing with applications to a practical business need. |
Tatiana Podladchikova | 6 | MA060186 | ||
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Fundamentals of Wireless Communications
The course "Fundamentals of Wireless Communications" continues the previous «Introduction to Wireless Communications course and describes the fundamental principles behind modern cellular systems (4G/5G). The main technologies of these communication systems are multicarrier modulations and multiple antenna techniques (MIMO: Multiple Input, Multiple Output). Given Shannon’s theorem (described in the "Information and Coding Theory" course), we can perform a theoretical analysis of different communication technologies in terms of the achievable rate of reliable communications. The channel capacity will be revisited for different communication scenarios. Given a multipath wireless channel, the diversity-multiplexing tradeoff (a tradeoff between reliability and a data rate) will be considered for multiple antenna systems.
Lecture materials consist of the following topics: 1) Power and band-limited communication scenarios 2) Capacity of fading channels 3) Frequency-selective wireless channel 4) Modulations for frequency-selective channels 5) Multicarrier modulations 5) Physical principles of MIMO 6) Capacity of MIMO channels 7) Diversity-multiplexing tradeoff for MIMO systems, and 8) Multiuser communications The practical part of the course includes a series of labs (in MATLAB and/or Python). Each lab suggests conducting a numerical experiment to evaluate the performance of different communication techniques. This course relies on the "Introduction to Wireless Communications,", "Digital Signal Processing,", and "Information and Coding Theory" courses. |
Kirill Andreev | 6 | MA060527 | ||
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Gas Recovery and Gas Hydrates
Natural gases characterization of the gas and gas-condensate fields and overview of technological complications (flow assurance) at different stages of field development.
Gas hydrates: physical and chemical properties, two- and three-phase equilibria, quadrupole points, phase diagrams of gas and the gas-condensate systems including water, ice and hydrates; gas hydrate control, thermodynamic (methanol, MEG, electrolytes) and low-dosage (kinetic and anti-agglomerates) inhibitors. Hydrate control at gas wells, gas gathering systems and the main technological processes of natural gas treatment in field conditions: dehydration of lean gases (absorption and adsorption); low-temperature processes of gas treatment at gas-condensate fields. |
Vladimir Istomin | 6 | MA060291 | ||
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History and Philosophy of Science. Candidate Exam
The aim of this course is to give to Skoltech students and postgraduates basic information about the main stages of the development of science from its birth in Ancient Greece through the Middle Ages and the Renaissance to Modern Times and to the great scientific revolutions of the XX century. Every man of culture especially a future scientist should know the impact of such great thinkers as Plato, Aristotle, Thomas Aquinas, John Buridan, Nicholas of Cusa, Copernicus, Galileo, Descartes, Newton, Boscovich, Darwin, Mendel, Bohr and Einstein (omitting many other brilliant names, that would be spoken about in the frames of the course) to the development of a scientific picture of the universe. Also there will be discussed the main topics and notions of the philosophy of science: demarcation between science and humanities, Popper’s theory of falsification, Kuhn’s theory of scientific revolutions, philosophical ideas of Lakatos and Feyerabend.
The course will consist of 18 3-hour lectures and 6 examination sessions (3 hour each). The students are to submit 6 written essays in English on the following themes: 1. Ancient Greek and Roman Science; 2. Medieval and Renaissance Science; 3. Scientific Revolution of the XVII century; 4. Science in the XVIII and XIX century; 5. Science in the XX-XXI centuries; 6. Philosophy of Science. For the final exam the students should prepare 10-15 minutes oral presentation on the scientific problem they are currently working upon, or on some topic connected with history of science and/or philosophy. Lectures 1-3 are devoted to the Ancient Greek and Roman Science Lectures 4-6 are devoted to Medieval and Renaissance Science Lectures 7-9 are devoted to the Scientific Revolution of the XVII century Lectures 10-12 are devoted to Science in the XVIII and XIX century Lectures 13-15 are devoted to Science in the XX-XXI centuries Lectures 16-18 are devoted to the Philosophy of Science |
Ivan Lupandin | 6 | DG060026 | ||
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Immunology
The purpose of the course “Immunology” is to lay the foundation for understanding the principles of organization, basic algorithms and rationale of the immune system. Such basis is necessary for the further professional growth either in the field of fundamental immunology or applied research and development in medical immunology. This course will also be important for those who wish to professionalize in medical practice, pharmaceutical industry, epidemiology and health services management, engineering and business in the field of modern biomedicine.
The course is focussed on the human immune system, towards clinical applications. The first part should help to form a systematic view of the architecture of the immune system. Next, the main medical aspects related to the functioning of the immune system will be considered, including autoimmune diseases, hypersensitivity reactions, cancer immunology, immunotherapy, vaccinations, autologous and allogeneic blood cells transplantation. Special attention paid to adaptive immunity and state-of-art bioinformatic approaches to the analysis of the T-cell receptor and antibody repertoire, the search for diagnostically and therapeutically relevant markers of adaptive immunity. In addition to the main program, PhD students and the boldest MS students will be offered to run a short, team-up, publication-oriented biomedical project. |
Dmitriy Chudakov | 6 | MA060172 | ||
| Integrable Many-Body Systems and Nonlinear Equations (Term 3-4) | Anton Zabrodin |
63 per term
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MA060602 | ||
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Integrated Silicon Photonics
Over the last decade, it has emerged that silicon is a fantastic material system for building photonic devices, as well as electronic ones. And, even more surprisingly, the silicon photonics community has developed process flows that permit the re-use of CMOS fabrication infrastructure to build complex photonic circuits. Silicon photonics is currently at the same early stage of expansion as electronics was in the 40 years ago, but with a major advantage for chip fabrication: existing silicon technologies that produce highly controlled wafers for microelectronics already exist.
The course will suggest a survey from fundamental properties of guiding and manipulating light on a chip to the design and fabrication of the photonic components on silicon on insulator platform. It includes passive silicon photonic components like gratings, directional couplers, arrayed waveguide gratings, and micro-ring resonators, active components like modulator, detectors, and laser hybridization. A photonic circuit response also includes effects due to the physical implementation, namely lithography effects, fabrication non-uniformity, temperature, waveguide lengths, and component placement. |
Vladimir Drachev | 3 | MA030581 | ||
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Intellectual Property in Industry. Patents, Patent Search and Drafting
This course focuses on the basics of patent search. This process is an integral part of a significant number of R&D projects. Patent search is also the most important type of engineering activity that accompanies any process of new inventions development and application.
In this course, students will be generally acquainted with the concepts and basics of intellectual property protecting, including industrial property. Also as part of this course, students will master necessary knowledge and skills in the field of patent search. The course will be useful to a wide range of specialists who develop new technical solutions and introduce them into production. |
Vadim Sulimov | 3 | MA030528 | ||
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Introduction to Blockchain
This course provide an overview of modern blockchain technology and its' practical applications (Cryptocurrency, Certification, Anchoring. Industrial examples.) We will start from basic cryptography and distributed data base systems and show how these tools are used in blockchain. The covered topics are the following:
-) Introduction to cryptography, type of ciphers. Private and Public crypto systems |
Alexey Frolov | 3 | MA030272 | ||
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Introduction to Digital Pharma
Modern pharma undergoes digital transformation. AI and other ‘digital’ technologies are actively applied in drug discovery and development to make the whole process less time consuming and more cost-effective.
The course will cover all aspects of drug design and development where digital technologies are being implemented: – Identification of drug target and its druggability assessment (genome mining), virtual high throughput screening. – Computer-aided drug design, combinatorial chemistry, exploration of chemical space. AI as a tool for chemical synthesis of new drug-like molecules. – Informatics approaches in prediction of the absorption, distribution, metabolism, elimination and toxicity (ADMET) of drug molecules. – Informatics approaches in pre-art and freedom to operate analysis. – Machine learning in clinical trials and drug repurposing. – Formulation Development – AI and Personalized Medicine. An invited lecture of company developing informatics solutions for research and development of new medicines is planned. On site visit to ChemRar High-Tech Center. At the end of the course students will know the whole pipeline of drug design and development in the pharmaceutical industry. As in general this field is dynamic with changes across R&D, clinical trials, manufacturing and regulatory processes, the AI is either already applied or will be introduced in the future. The students will get hands on experience in several particular areas of drug design with specific drug target/s. The visit to the high-tech center will demonstrate how this business works in real life. |
Natalia Strushkevich | 6 | MA060418 | ||
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Introduction to IoT
In the last decade the Internet of Things (IoT) paradigm has slowly but steadily and increasingly permeated what researchers and engineers study and build. The term “Internet of Things” doesn’t have a single definition and people today often use it to interchangeably refer to Wireless Sensor Network (WSN), Machine-to-Machine (M2M), Web of Things (WoT) and other concepts. The focus of this course is to learn about these technologies that will be extending the Internet as we know it and use it today, to interconnect not only people and computers but also sensors and associated objects. The course will be divided into two strongly coupled parts. The first part of the course covers the IoT ‘pillar’ technologies, i.e. embedded systems, wireless sensor networks, semantic technologies, and theory behind them while the second part will have a special focus on IoT development, i.e. IoT apps, open platforms, sensors and actuators, software/middleware. Apart from covering the theory behind the IoT and “how to connect things to the Internet”, the course will therefore also engage the students to demonstrate the feasibility of simple IoT real applications and will challenge them to improve their applications through the use of cognitive technologies and cloud computing.
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Andrey Somov | 3 | MA030233 | ||
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Introduction to Quantum Field Theory (Term 3-4)
As you know, the modern theory of fundamental physics (the "standard model of elementary particle physics") is a quantum field theory (QFT). In addition to this central role in modern physics, quantum field theory also has many applications in pure mathematics (for example, from it came the so-called quantum knot invariants and Gromov-Witten invariants of symplectic manifolds).
"Ordinary" quantum mechanics deals with systems with a fixed number of particles. In QFP, the objects of study are fields (not in the sense of a "field of complex numbers", but in the sense of an "electromagnetic field"), whose elementary perturbations are analogs of quantum mechanical particles, but can appear and disappear ("born" and "die"); at the same time, the number of degrees of freedom turns out to be infinite. Within the framework of this course, the basic concepts of QFT will be introduced "from scratch". The Fock space and the formalism of operators on it, as well as the formalism of the "continuum integral" will be defined. The main example under consideration will be the quantum scalar field theory. A scalar field in physical terminology is a field that, at the classical level, is defined by one number at each point (i.e., in fact, its state at a given time is just a numerical function on space), unlike a vector field (an example of which, in particular, is an electromagnetic field). However, considering the quantum theory of a scalar field (even separately, and simpler than for the Higgs field) is in any case very useful, since it allows you to get acquainted with the apparatus and phenomena of QFT on a simpler example than vector and spinor fields. The course will consider the "perturbation theory" (that is, in fact, a method for calculating the first orders of smallness in a small parameter expansion) for a scalar field and describe ways to calculate various probabilities of events with particles. |
Vladimir Losyakov |
63 per term
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MA060505 | ||
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Introduction to Recommender Systems
This course provides a high-level overview of the recommender systems field. It covers the key topics of recommender systems development from theoretical and practical viewpoints. The course aims to guide students through the landscape of different recommender systems types, methods, and applications and equip them with the necessary knowledge for creating practical solutions. Students will learn how to translate recommendation tasks from real-world personalization services into suitable mathematical formulations and solve the corresponding problems with the help of proper techniques, algorithms, and software tools.
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Evgeny Frolov | 3 | MA030499 | ||
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Laser Physics
The purpose of the course is to provide a solid background in the laser physics with strong emphasis on lasers for applications. Lectures cover various aspects of modern laser physics including laser dynamics; ultra-fast lasers; Ti:Sapphire, fiber and semiconductor lasers; high-power lasers; wavelength conversion and supercontinuum generation. The course will be focusing on a practical skills. During the lectures there will be a lot of teamworking, problem solving and estimations. On practical seminars we will assemble our own femtosecond fiber laser and measure its emission properties.
After the course students will be acquainted with the main principles of the laser operation, will obtain the a hand-on experience in assembling fiber lasers and measure the ultrashort pulse parameters and we be able the find a suitable laser system for their particular application. |
Yuriy Gladush | 6 | MA060143 | ||
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Machine Learning
The course is a general introduction to machine learning (ML) and its applications. It covers fundamental topics in ML and describes the most important algorithmic basis and tools. It also provides important aspects of the algorithms’ applications. The course starts with an overview of canonical ML applications and problems, learning scenarios, etc. Next, we discuss in-depth fundamental ML algorithms for classification, regression, clustering, etc., their properties, and practical applications. The last part of the course is devoted to advanced ML topics such as Gaussian processes, neural networks. Within practical sections, we show how to use the ML methods and tune their hyper-parameters. Home assignments include the application of existing algorithms to solve data analysis problems. The students are assumed to be familiar with basic concepts in linear algebra, probability, real analysis, optimization, and python programming.
On completion of the course students are expected to: |
Evgeny Burnaev | 6 | MA060018 | ||
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Master Your Thesis in English
Writing a Master's Thesis is a challenging job, so we encourage you to start working on it early enough to make a quality product. As you know, Skoltech requires a draft of the Literature Review and other chapters by January, which is only a few months away. Make the maximum of the time available and join the course 'First Steps to Thesis in English'!
The Course offers concise and practical guidelines for making the first steps in writing a Master's Thesis at Skoltech. Minimum theory. Maximum practice. Thorough and individualized feedback. Students will analyze authentic papers in their disciplines and learn to emulate the best examples. Participants will get insights into the structure, vocabulary, and grammar of each part of the thesis; they will edit and improve their text based on the instructor’s feedback. By the end of the course, a successful student will: – Develop linguistic awareness needed to avoid the typical pitfalls in writing and live presentations; |
Elizaveta Tikhomirova | 3 | ME030567 | ||
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MIMO Systems in Wireless Communications
This course provides an overview of modern spatial processing techniques in wireless communication systems, and shows technological aspects of massive-MIMO systems. In addition, it will be studied 3GPP standard evolution to feel requirements of high precision massive-MIMO systems from channel acquisition accuracy to algorithm robustness in case of different error influence.
We will start from basic principles of wireless communication systems; will study Shannon’s capacity, and its redefinition in terms of independent and correlated spatial channels, then move to space-time coding, spatial filters, channel acquisition techniques in 4G and 5G NR systems. Final part of the course will be denoted to specifics of radio resource management in massive-MIMO systems. After this course, the students have to be familiar with the main principles of MIMO systems in wireless communication, understand advantages of spatial domain usage, and feel the main challenges in system level and computational aspects. |
Vladimir Lyashev | 3 | MA030412 | ||
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Modern Dynamical Systems (Term 3-4)
Dynamical systems in our course will be presented mainly not as an independent branch of mathematics but as a very powerful tool that can be applied in geometry, topology, probability, analysis, number theory and physics. We consciously decided to sacrifice some classical chapters of ergodic theory and to introduce the most important dynamical notions and ideas in the geometric and topological context already intuitively familiar to our audience. As a compensation, we will show applications of dynamics to important problems in other mathematical disciplines. We hope to arrive at the end of the course to the most recent advances in dynamics and geometry and to present (at least informally) some of results of A. Avila, A. Eskin, M. Kontsevich, M. Mirzakhani, G. Margulis.
In accordance with this strategy, the course comprises several blocks closely related to each other. The first three of them (including very short introduction) are mainly mandatory. The decision, which of the topics listed below these three blocks would depend on the background and interests of the audience. |
Alexandra Skripchenko |
63 per term
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MA060257 | ||
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Neuroendocrinology
Neuroendocrinology is thе part of physiology dealing with interactions between the nervous and the endocrine system. These include interactions on molecular cellular and organism levels controlling physiological processes of the human body. This course will give advanced understanding of normal physiology and pathophysiology of the brain as an endocrine organ. The special attention will be given to hypothalamus/pituitary/adrenal axis and gonads, which maintain homeostasis, regulating reproduction, metabolism, energy utilization, control of cardiovascular system and other functions.
Modern methods in cardiac physiology, renal physiology, vascular biology. Students will learn most important techniques to study heart function, excretion and regulation of vascular resistance: ultrasound and MRI imaging, telemetry, single nephrone studies, wire myography and others. |
Yuri Kotelevtsev | 3 | MA030402 | ||
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Neurotechnology Lab Course
A laboratory-based course. Neurotechnology Lab Course is directed to overview the common and advanced Neurotechnological techniques, different types of EEG-based brain-computer interfaces (BCIs). The course consists of a short theoretical part, workshops, and a number of laboratory experiments. It includes a group project and is highly focused on getting practical skills in experimental design, neurophysiological data recording, modern neuromodulation techniques and analysis of experimental data with the aim to build brain-computer interfacing systems.
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Nikolay Koshev | 6 | MA060022b | ||
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Next Generation Sequencing – Experimental Protocols and Data Analysis
Next generation sequencing is a group of methods that allow simultaneous sequencing of many thousands of DNA fragments without their physical separation by cloning. In the last 15 years it revolutionized many fields of biology: genetics, evolutionary biology, microbiology, anthropology. It have also many practical applications, especially in biomedicine. However NGS and corresponding bioinformatics methods are sometimes used in a black box regime, without understanding its logic, the area of applicability and limitations. This course will provide a comprehensive survey of both experimental aspects of NGS and of bioinformatics analysis of the data. We will also discuss the current trends in the development of sequencing technologies and their applications. The practical part of the course will offer a hands-on experience of a genomic project, starting from the experiment design, followed by preparation of DNA and RNA samples, sequencing, de novo genome assembly, annotation and analysis of differential expression.
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Maria Logacheva | 3 | MA030396 |
MOVEDmoved to Term 2
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Non-Equilibrium Processes in Energy Conversion
Classical thermodynamics is useful to describe equilibrium states, while non-equilibrium states and irreversibility characterize real physical processes. If one is interested in actual processes at work during energy conversion, a classical thermodynamic description of equilibrium states is insufficient as it yields very incomplete information on the processes. Irreversible thermodynamics accounts for the rates of physical processes, and provides relationships between "measurable quantities" such as transport coefficients. This graduate course, which constitutes the natural continuation of the course Energy Systems Physics & Engineering, provides the students with basic knowledge of out-of-equilibrium and finite-time thermodynamics, which describe irreversible processes that routinely take place in physical systems and permits a fine understanding of the processes ensuring energy conversion. Thermoelectric generators serve as the main example to illustrate in a simple fashion the out-of-equilibrium formalism, and other systems such as, e.g., solar cells are studied.
Essential notions which are taught include: Onsager’s approach to linear nonequilibrium thermodynamics; coupled transport theory; Boltzmann equation; thermal conductivity; electrical conductivity; electrochemical potential in solid-state systems; force-flux formalism and its application to thermoelectric systems; device optimization modelling accounting for dissipative coupling to heat reservoirs. The course is based both on "teaching with lecture" and "teaching with discussions" methods. In addition to home assignments, students will solve problems during tutorials and discuss their solutions. If, as for the academic year 2020-2021, the course is given in online mode, much emphasis will be put on the "teaching with discussion" to mitigate risks of superficial learning. |
Henni Ouerdane | 6 | DA060200 | ||
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Numerical Methods for Conservation Laws (Term 3-4)
The course is an introduction to the mathematical theory and numerical analysis of conservation laws that arise in applied sciences. It begins with a theory of conservation laws, which makes a first of two main parts of the course. Origin of conservation and balance laws in mechanics, physics, chemistry, biology, and other fields of science is explained. Then a theory of scalar conservation laws is developed with an introduction of such concepts as: hyperbolicity, weak solutions, shock waves and rarefactions, entropy conditions. Important nonlinear systems such as Euler equations of gasdynamics, shallow water equations, two-phase flow models, traffic flow models are analyzed. Theory of linear hyperbolic systems is given, including the solution to the Riemann problem. Then nonlinear systems are analyzed in terms of the existence and properties of shock waves, rarefactions, and the solution of the Riemann problem, including one for the 1D Euler equations of gasdynamics. The second part introduces numerical methods for conservation laws: methods for linear systems, stability, the Lax equivalence theorem, upwinding, modified equations, conservative methods, the Lax-Wendroff theorem, entropy condition, Godunov's method, approximate Riemann solvers, nonlinear stability, TVD methods, monotone methods, high-resolution methods, flux limiters, slope limiters, semi-discrete methods, ENO/WENO methods, multi-dimensional problems.
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Aslan Kasimov |
63 per term
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MA060574 | ||
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Numerical Methods in Engineering and Applied Science (Term 2-3)
The course is intended to provide the understanding and working knowledge of
numerical methods required for modeling and simulation of complex phenomena described by ordinary and partial differential equations. The course focuses on understanding fundamentals of numerical methods such as accuracy, stability, convergence, and consistency rather than learning how to use canned computer codes. The course involves a fair amount of first-hand experience with programing and solving real problems on computers. Although the solid knowledge of calculus, linear algebra, complex variables is essential, only basic understanding of the theory of ordinary and partial differential equations as governing equations for physical and engineering systems as well as basic programming skills are required. The following topics are discussed: interpolation, numerical differentiation, numerical integration, numerical solutions of ordinary differential equations, and numerical solution of partial differential equations. Students will have to complete computer projects, mid-term and final exams. |
Oleg Vasilyev |
63 per term
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DA060239 | ||
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Numerical Modeling
Many scientific models are formulated in terms of differential or integral equations and describe continuous quantities, such as the distribution of velocity of a fluid in a space outside an aircraft wing, distribution of stress in a solid body, price of a stock as a function of time, etc. In order to use these models in a computer simulation, the models must be discretized. The course covers a representative selection of methods of discretization of differential and integral equations. The emphasis of the course is on practical aspects of using discretization methods: intuitive understanding and formal derivation of accuracy of different methods, modelling, testing and optimizing real mechanical systems, and solving applications-informed practical problems.
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Alexander Shapeev | 6 | MA060005 | ||
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Omics Data Analysis (Term 3-4)
An avalanche of ‘omics data is coming from different sources: transcriptomics, epigenomics, lipidomics, metabolomics. A thorough analysis of such large-scale biological data sets can lead to the discovery of important biological insights on mechanisms of cell and organ functioning, and to the identification of small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions. The course will discuss general concepts of bioinformatics analysis of various types of ‘omics data and will provide examples of their successful application for solving a wide range of biological problems. The students will not only learn the best analysis practices suitable for each data type but, importantly, understand why each analysis step is necessary, what is the logic of the whole data analysis concept, which controls are essential, etc. The course will end with the task (Final Project) on the integration of different data types produced to answer the same biological question.
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Ekaterina Khrameeva |
63 per term
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MA060061 | ||
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Optical Communications. Basics
This course is the first part of a series devoted to modern optical communication systems. The aim of the course is to study the fundamental physical principles underlying modern optical communication systems. Further courses in this series will be devoted to more applied (engineering) issues in the field of optical information transmission systems. The proposed course will give the necessary knowledge from the theory of information, universal for any (not only optical) data transmission systems, in particular the Shannon theorem. Based on a unified description of stochastic processes, sources of errors in digital signal transmission will be systematically considered. Fundamentals of quantum mechanics of open systems based on density matrix will be systematically described. Using this approach, the dynamics of lasers and optical amplifiers as applied to optical information transmission systems will be analyzed. The main factors limiting the capacity and distance of the transmitted signal will be considered. In particular, the linear and nonlinear properties of the medium (mainly on the example of optical fibers) that affect the parameters of the propagating signal will be analyzed. The course will also consider modern issues of physics and technology of information transmission systems, including quantum methods to ensure the confidentiality of the transmitted signal, applications of nanophotonics and photonic integrated circuits, neural optical networks, radio photonics, as well as the role of photonics in the development of mobile networks of the fifth and sixth generations.
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Arkady Shipulin | 3 | MA030500 | ||
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Optimization Methods
The course is devoted to optimization methods and optimization problems design with a special attention to those motivated by control sciences, energy systems and other engineering applications.
The course starts with a brief annotation of magor concepts: convexity, convergence, optimization models. Then we discuss zero, first and second order methods with a special focus on their efficient implementation. We distinguish between various problem classes, discuss suitable methods for every class. The problem formulation and its proper reformulation is the critical issue for an optimizer. We’ll learn about optimization models and convex relaxations. Special attention will be addressed to Linear Matrix Inequalities (LMI) that arise in optimization problem formulations. One of the home assignments is devoted to understanding the constraints of performance for different software packages. |
Elena Gryazina | 6 | MA060002 | ||
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Organic Chemistry for Energy Materials
The suggested course is an introduction to the main topics of organic materials used in energy storage and conversion and covers basic knowledge about them (physical and electrochemical properties, application and characterization, molecular design).
Skoltech graduates students in the field of materials science. Today, the students study several courses on inorganic materials used for energy storage. The suggested course will expand their knowledge of organic materials used in energy technology. |
Olga Shmatova | 3 | MA030590 | ||
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course describes the intended learning outcomes and how they are assessed.
The main bulk of the 81 hours of the course is spent in the actual courses in which the PhD-students do their TA-assignments. The assignments in the course itself include TA proposal and TA report and for cohorts 2022-2025 participation in training: Towards excellence in Teaching Assistantship”. Educational department should approve all assignments in Canvas in terms of content and formal requirements respectively. |
Dmitry Artamonov | 3 | DG030005 | ||
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Perception in Robotics
This course will present the fundamental theory and application of perception techniques. The word perception on the context of this course will refer to the problems of Localization, Mapping and in general State Estimation. Today we are witnessing an explosion on the applications for this technology, originally developed on the robotics field, being outsourced to other domains such as self-driving cars, augmented reality, flying drones, etc. Yet there are many challenges to be solved on a wide variety of research topics. The content of the course will be mainly based on a probabilistic approach to perception problems and will examine a selected set of contemporary algorithms in depth. Topics include Bayesian filtering; Batch Estimation, Mapping, localization and simultaneous localization and Mapping (SLAM); Observation and transition functions; Read below about the course policy, final project and other details.
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Gonzalo Ferrer | 6 | MA060283 | ||
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Petroleum Geophysics
The main objective of the course is to give an overview of the main geophysical methods used for different industrial applications with the main emphasize of the cases of petroleum and geothermal exploration. The course includes a brief theoretical background of each method, general workflow of data acquisition and processing, examples of case studies from recent scientific articles and possible applications in industry. The course starts with the descriptions of the main targets of geophysical surveys consisting in mapping the shapes of underground geological structures and determination of physical properties of rocks (density, susceptibility, resistivity, and seismic wave velocities). The general principles of the forward and inverse problem solutions are presented. The course covers three general groups of geophysical methods based on 1)seismic, 2)electromagnetic and 3)natural field data.
Within the seismic part, we will consider different aspects of reflection seismic exploration, including the Common-Deep-Point standard workflow, migration problems, AVO analysis etc. This part also includes the methods based of diffracted, scattered, refracted and head wave data analysis. Passive source seismic surveys and seismic interferometry methods are also presented in this part of the course. Within the electromagnetic part, we consider a broad variety of different methods and techniques, such as vertical electrical sounding, electrical resistivity tomography, inductivity methods, georadar technology, transient and time-domain methods, and magnetotelluric sounding. This part of the course includes a variety of examples of practical applications of each method in petroleum exploration and other industrial fields. Natural field part includes multiscale studies based on the gravity and magnetic fields. We also consider existing methods for the heat flow assessment. Finally, measurements of ground deformations based on the leveling, GPS and InSAR data are presented. |
Ivan Kulakov | 6 | MA060561 |
MOVEDmoved to Term 4
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Petrophysics and Well Log Interpretation
The course provides both theoretical knowledge in well logging application for reservoir characterization and practical skills in well logging processing and interpretation software.
During the course students will get familiar with the main well logging tools and methods, such as gamma, spontaneous potential, caliper, formation density, neutron, electrical, formation micro-imaging, sonic, magnetic resonance logging and so on. The students will study the well logs application for defining the key reservoir properties, correlation, integration with laboratory core test data. Besides lectures the course includes computer classes where students will gain applied skills in well logging interpretation software (Techlog). During the software practicums the participants will learn the main well logging data handling operations, such as creating a project for a field, import / export, displaying logs, data and variable management, depth referencing, constructing cross-plots, calculating parameters, well correlation. The proportion of lectures and software classes is approximately 50% / 50%. |
Alexei Tchistiakov | 3 | MA030289 | ||
| Phase Transitions, Rigorous (Term 3-4) | Semen Shlosman |
63 per term
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MA060600 | MOVED FROM T1-2 | |
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Photonics Research Seminar Series (Term 1B-4)
The course "Photonics Research Seminar Series" aims to provide MSc and PhDs with a broader perspective into state-of-the-art research in photonics, from fundamental insights (both theoretical and experimental) to engineering and applications. The course also aims to train students in how to structure scientific results, how to provide a coherent narrative and how to appropriately present it in the form of a strictly-timed oral presentation. The bulk of the course consists of a weekly one hour seminar on seminal topics of photonics, from guest speakers or Skoltech faculty, researchers and students.
The course is a prerequisite for the research thesis defense of projects of the following laboratories (and is as such compulsory only for the 2nd year MSc students of these laboratories): |
Pavlos Lagoudakis |
30.75 per term
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MA030553 | ||
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Physics of Colloids and Interfaces
Interface science is the basis for modern nanotechnology. Objects of the microworld are dominated by surface effects rather than gravitation and inertia. The applications of interface science are important for lab-on-chip technologies, microfluidics, biochips, tissue engineering, biophotonics, theranostics. Modern interface science is a good example of interdisciplinarity: it includes physics, chemical engineering, biology, medicine. During this course, the students gain not only theoretical knowledge but also receive practical skills related to:
– surface tension measurements; – contact angle measurements; – surface potential measurement by Kelvin probe method; – nanoparticle characterization by dynamic light scattering method for determination of size and Z-potential of nanoparticles; – synthesis of calcium carbonate cores at the micron- and submicron size and loading of calcium carbonate particles by inorganic nanoparticles and proteins; – fabrication of polymer and nanocomposite microcapsule shells by the Layer-by-Layer assembly approach. They will receive knowledge that can be used for the analysis of phenomena in the microworld from point of view of interface science. |
Dmitry Gorin | 6 | MA060524 | ||
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PLM 1 - Digital Technologies in Conceptual Design of Cyber-physical Systems
Basic course for 1st year MSc students devoted to PLM as applied to product development. Lectures are devoted to an overview of current trends in industry digitalization, “digital twins” technology and modern implementation of computer-aided design, computer-aided engineering, computer-aided manufacturing, model-based systems engineering, product lifecycle management, multidisciplinary optimization, predictive and prescriptive maintenance. Practical classes are dedicated to the simulation-driven product development process in a particular case study. Students learn how to develop a high-level model of a complex system, split it into subsystems and connect it with the functional models of each subsystem and with preliminary 3D models (e.g. aerodynamics and structural analysis). Also, the optimization of the whole system plays an important role in the course. The case study is a challenging task like High-Altitude Pseudo Satellite, Truss-Braced wing aircraft or Fuel cell powered multicopter. Thus, during the course students go through all the main stages of complex system development process.
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Clement Fortin | 6 | MA060535 | ||
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Power Electronics
The course provides an overview of the latest achievements in power electronics. The main purpose of the course is to analyze different circuit topologies, to understand how they work and which are their benefits and limitations. The course starts with reviewing the basics in electric circuit theory, and then, it introduces different kind of semiconductor devices such as diodes, thyristors and transistors. After this, power electronics circuits are presented: rectifiers, DC-DC converters and inverters. The course gives the tools to analyze any kind of power converters, and provides different examples related with microgrids and energy storage applications. It has three parts: lectures, home tasks and experimental activities in the lab. By the end of the course, the students should be able to analyze a power converter, to simulate it and to understand the possible applications.
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Federico Martin Ibanez | 6 | MA060198 | ||
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Power Markets and Regulations
The course will introduce the students to power system economics. After covering the fundamentals of microeconomics, the main types of electricity markets and regulations will be discussed including the Russian market. Economic dispatch and Optimal Power Flow with Locational Marginal Pricing will be covered. Capacity planning, ancillary services, and risk analysis are also covered.
The lectures will be supplemented by homeworks utilizing PowerWorld simulation package, a laboratory exercises investigating gaming in power markets and group mini-projects. |
Marina Dolmatova | 6 | MA060441 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Nikolay Brilliantov | 3 | DD030020cd | ||
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Qualifying Exam: Engineering Systems
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Henni Ouerdane | 3 | DD030020es | ||
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Qualifying Exam: Life Sciences
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Mikhail Gelfand | 3 | DD030020ls | ||
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Qualifying Exam: Materials Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Artem Abakumov | 3 | DD030020ms | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Mikhail Spasennykh | 3 | DD030020pe | ||
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Qualifying Exam: Physics
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Nikolay Gippius | 3 | DD030020p | ||
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Research Methodology for Engineering Systems
Engineering systems are necessarily of an interdisciplinary nature and specific knowledge and skills are required to successfully complete a PhD degree in the field. The objective of this course is to provide a general introduction to PhD level research and to present some common research methodologies applicable to major types of research topics in Engineering Systems. The course starts with a series of lectures of an introductory nature on the Design Research Methodology (DRM) and other experimental and simulation based approaches.
Participants will then present their preliminary research ideas, propose research objectives and proper methodology that they will present to their peers first in a pitch session; it will be followed by a more detailed research proposal and presentation. The course can be followed by a subsequent series of research seminars in various topics related to Engineering Systems as indicated by the PhD student supervisor. |
Clement Fortin | 3 | DG030102cf | ||
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Research Methodology: CMT Research Seminar (Term 2-4)
This is the main scientific seminar for the Skoltech Center for Materials Technologies (CMT). All MSc students either enrolled into the Master Program in Advanced Manufacturing Technologies or PhD students affiliated with CMT should attend this seminar. The format of the seminar is weekly invited lectures from top scientists in the research fields related to Advanced Manufacturing, Digital Engineering Technologies, and Mechanics and Physics of Advanced Manufacturing. The seminar is held in hybrid online/offline format.
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Ivan Sergeichev |
31 per term
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DG030102dm | ||
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Research Methodology: CPSE Seminar
This course, conducted in a form of seminar, is aimed at hands-on learning of best practices of modern research work in the academic and industrial environments. Students will learn how to formulate research objective(s), search for prior art and monitor publications, frame a research project with proper objectives, resources, timeline, and deliverables, conduct research in communication with peers and with a broader community, and, finally, present the outcomes of the research.
Students will have an extensive practice in writing reports and publications, as well as in oral presentations. By attending this course, students will get a broader view of the research projects conducted in CPSE, as well as a closer scientific communication with their peers and with a larger research community. The seminar work constitutes the core of the course, and it will be given principally on the basis of all the topics related to petroleum engineering. The cases will be presented based on Enhanced oil recovery (EOR)/Improved oil recovery (IOR), interfacial properties in oil/water/rock system, CCUS (carbon capture, utilization and storage), and hydrogen production. |
Chengdong Yuan | 3 | DG030102pe | ||
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Research Practice Seminar "Energy Systems and Technologies" (Term 2-4)
This research seminar is the general meeting for faculty, researchers and master and PhD students of Energy Systems programs. The seminar takes place every week during Terms 2(6)-3(7)-4(8).
Master students must attend the seminar at least for one academic year but welcome to attend during two years. PhD students are welcome to attend the seminar during all years of studies but can gain no more than 6 credits in total. The seminar consists of faculty lectures, invited lectures of top scientists in their research field as well as students’ reports on their own or examined papers. To PASS the course and gain 3 credits per academic year the student must fulfill all three requirements: 1. Attendance: > 2/3 of seminars. 2. Presentation. Depending on the status: 3. Active participation in the discussion. Being able to pose a question for every presentaion. The core of the self-study activity will be preparation to the talk that is comparable to project implementation (a significant part of many regular courses). The students are expected to assign at the beginning of Term 2/6 and may drop the seminar till the beginning of Term 3/7 while credits are provided in Term 4/8. |
Elena Gryazina |
31 per term
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MA030489 | ||
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Research Seminar "Advanced Materials Science" (Term 1B-4)
This is a research seminar of the Skoltech Center for Energy Science and Technology and Materials Science Education program featuring presentations of young Skoltech researchers (MSc students, PhD students, postdocs) as well as external invited speakers. Every MSc and PhD student should deliver at least one presentation during the course. The range of topics is broad and includes any aspects of materials science. As a rule, each speaker presents results of his/her own research with a particular focus on research methodology. Every presentation is followed by a discussion.
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Sergey Luchkin |
30.75 per term
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DG030302i | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, cluster algebras etc.
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Andrey Marshakov |
61.5 per term
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DG060268 | ||
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Review of Materials and Devices for Nano- and Optoelectronics
The lectures are presented by the scientists working actively in various directions of nanoelectronics and optoelectronics in Russia and abroad. Seminars assume the discussion of recent original papers in the area (including superconducting electronics and spintronics, terahertz waves technology and applications, quantum coherent systems (qubits), single electron devices). The classical papers presenting a physical basis for devices operation will be also considered. The papers are distributed in February. Each student is expected to report two papers during the period February-May if participating in both parts of the course (Terms 3 and 4), and only one paper if participation is limited to one term.
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Valery Ryazanov | 3 | MA030206 | ||
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Satellite Navigation
The GNSS data processing course is aimed to cover the modern state of technology in global navigation satellite systems applications for precise surveying, agriculture robotics, road construction and other applications. Course participants will become familiar with subjects such as:
– Navigation observables and navigation solution, – Single-, double-, triple difference techniques, – Precise point positioning, – Real-time relative positioning, – Carrier phase ambiguity resolution and integer lattice reduction, – Anomalies detection and isolation, – Basics of satellite receivers firmware design, – Geodetic surveying and practical using of GNSS receivers for accurate surveying, – Using GNSS navigation for attitude determination and motion control for wheeled robots, UAV's and other applications. |
Tatiana Podladchikova, Maria Kudryashova |
6 | DA060380 | ||
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Signal Processing for Communications
This course is about signal processing application in wireless. It is designed to acquaint students with current wireless communication system techniques and how they are implemented.
During a short period of time, signal processing has emerged as a significant and quickly expanding study area in wireless communications. In a few years, it is anticipated that the use of modern signal processing techniques in wireless communications—such as compressed sensing and machine learning—will significantly alter the field. We examine the most exciting use cases of signal processing in the 5G and 6G system, and encourage students to implement some of them in Matlab or Python using the real-world data and cutting-edge algorithms. |
Andrey Ivanov | 3 | MA030592 | ||
| Some Uses of Twistors in Field Theory (Term 3-4) | Alexey Rosly |
63 per term
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MA060601 | ||
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Spacecraft and Mission Design
The main objective of the course is to introduce the concept of space system design and engineering. The course will describe the various subsystems involved in the design of a satellite. It will also describe the techniques of systems engineering that are used to obtain a coherent satellite design.
This class will focus on concept preparation in the V-diagram logic. Further results can be explored either in the Space Sector course, where commercial aspects of the mission can be considered, as well as in the PLM course, where technical details can be worked out in a systematic fashion. |
Tatiana Podladchikova | 6 | MA060074 | ||
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Spectroscopy of Quantum Materials
The term “quantum materials” unites a broad class of very different materials demonstrating genuinely quantum behavior. Quantum materials include superconductors, strongly correlated systems, systems hosting Dirac electrons, topological materials, novel two-dimensional crystals etc. Research of quantum materials stands in the vanguard of modern photonics and condensed matter physics.
The goal of this course is to give a broad review of basic models and modern spectroscopic studies of quantum materials. The course requires basic knowledge of quantum mechanics, optics and solid state physics. In the introductory part of the course, the basic classical and quantum models of electromagnetic response are considered, and the most widely used modern methods of spectroscopy are outlined. The rest of the course describes such quantum materials as graphene and graphene-based structures, topological insulators, topological Dirac and Weyl semimetals, strongly correlated materials, two-dimensional transition metal dichalcogenides, oxide interfaces, and novel engineered quantum materials. The students will be introduced to basic models and to both conventional and ultrafast pump-probe spectroscopy studies of these materials. |
Alexey Sokolik | 3 | MA030162 | ||
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Startups LaunchPad: DeepTech and Digital
“Startups LaunchPad: DeepTech & Digital” is an intensive 6 credits E&I course. It is designed to provide you with practical skills and experiences of translating your favorite technologies and discoveries into new products and then technology-based startups. The course uses “learning-by-doing” approach. This means you will learn how to build new products and startups by actually building ones. In small teams you will run real-life projects performing all the key activities of early stage startups – from “getting out of the building” (literally!!! your team is to talk to customers, partners, users, etc.) and validating the market need, to defining the technological solution and product, producing prototypes, designing profitable business model.
Admission to the course is by team and by project. The project themes may vastly range within the AI/IoT/Photonics/Materials/etc. (e.g. deal with energy, healthcare & wellness, education technology, industry or smart agriculture, etc.). We suggest forming the projects around ideas inspired by your academic research, technologies you studied at previous courses, prototypes you developed, etc. In all cases, you should choose something for which you have passion, enthusiasm, as well as technical expertise. By taking this course you will get the benefits: |
Alexey Nikolaev | 6 | E&I | MC060545 | |
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Structural Bioinformatics
The main goal of the "Structural Bioinformatics" course is to introduce students to the main features of protein structures and to the explanations of the observed features.
The "Structural Bioinformatics" course covers the following topics: In a more theoretical part of the course, we will overview (i) protein primary, secondary and tertiary structures, (ii) interactions stabilizing protein native structure (discussing the role of energy and entropy), (iii) statistical patterns observed in protein structures, (iv) protein thermodynamics, (v) protein kinetics, (vi) protein folding problem, and others. We will also discuss the design and the results of selected experiments in the field. In a more practical part of the course, we will (i) visualize protein three-dimensional structures, (ii) align protein sequences and structures, (iii) predict three-dimensional protein structure from sequence, (iv) predict protein cellular localization and transmembrane topology, (v) predict the impact of a mutation on protein stability, and others. |
Dmitry Ivankov | 6 | MA060375 | ||
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Structure and Properties of Materials
This course is an introductory subject in the field of materials science and crystallography. The goal is to introduce students to basic concepts of structure-property relations for materials at the microscopic level.
Independent student work on discipline includes preparation for lectures, seminars, labs and other learning activities, as well as the implementation of individual tasks / independent works / projects and others. Educational and methodical support of Independent student work presented by topics of all kinds of tasks and guidelines for their implementation. |
Artem Oganov | 6 | MA060075 | ||
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Technology Entrepreneurship Seminar: Advanced (Term 3-4)
Students having their own projects/ideas in development take this course to understand and practice advanced topics of technology entrepreneurship as well as to get the projects up to “external support/funding ready” level. This class typically follows the main E&I classes (e.g. as “Innovation Workshop” and “Technology Entrepreneurship Seminar: Foundation”, where students passed a basic opportunity recognition screening and customer discovery) and allows dedicated teams to continue development of their projects. Structured as practical and hands-on lab, the course typically has up to 6-7 projects under development. Students have the opportunity to present key “building blocks” of their ventures, receive advice from instructors and other members of the class, consult to other venture teams.
Admission to the course is by team and by project. We will select the participants from the teams took part in the course “Technology Entrepreneurship Seminar: Foundation” as well as newly applying teams. Upon the course completion, you and your team will |
Alexey Nikolaev |
31.5 per term
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E&I | MC030029b | |
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Tensor Decompositions and Tensor Networks in Artificial Intelligence
This course covers major topics in tensor decompositions and tensor networks for modern applications in Machine learning (ML), Signal Processing (SP), Deep Neural Networks (DNN), Multiviews, and Data Fusion.
The goal of the course is to provide students with a background in mathematics, especially in linear and multilinear algebra, statistics, useful computational tools. The emphasis of this course is on "learning by experimenting and programming". Students will learn various tensor decomposition models and state-of-the-art algorithms for each model. Students will study also challenging problems, e.g., degeneracy, stability, model selection, and know how to deal with them in practice, make the models scalable for big data, solve the complex tensor networks. Lectures will be strongly coupled with a number of hands-on sessions. Students will have the opportunity to understand how tensor networks are applied to Machine Learning problems, e.g., for Deep Learning and NLP, Signal Processing applications. Topics: Background: Basic tensor decomposition methods: Practical applications: |
Anh Huy Phan, Andrzej Cichocki |
6 | MA060468 | ||
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Thermodynamics and Transport at Nanoscale
The course introduces students to basic principles of thermodynamics and transport in “small systems, that is, the system whose size is comparable to the size of its major elements (be those molecules, micelles, polymer coils, etc). It covers small droplets, bubbles and crystals, stability of thin films, adsorption and deformation in nanoporous materials, stability of nanocolloids and nanocomposites, as well as transport and flows in micro-and nano- porous networks. In the process of studies, the students will also learn/reiterate the basics of interfacial thermodynamics, adsorption, wettability, spreading as well as the basics of transport of ionic and nonionic compounds in confined media. The lectures, seminars, homeworks and the final project will give practical example of calculations for all major classes of nanomaterials: nanoparticles and nanocomposites, microporous crystals, ordered and disordered mesoporous solids, and, finally nanostructured polymeric materials.
The course will be useful to all students willing to improve their understanding of natural (e.g. mineral oil) and man-made colloids, such as suspensions, emulsions and foams, as well as natural and synthetic porous materials. "Lectures" in the schedule refer to approximately 3h "windows" they will are not necessary scheduled as such, but will spread out and intermitted by seminars / recitations / HW and project discussions |
Vladimir Palyulin | 3 | MA030288 | MOVED FROM TERM 4 | |
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 |
Term 4
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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3D Bioprinting: Processes, Materials, and Applications
The course aim is to show the relationship of knowledge from material science, additive manufacturing, biology and medicine for description the processes, methods and results of bioprinting. Main course part will be devoted to the bioprinting processes and biomaterials.
We will describe the three key stages in 3D bioprinting, which are pre-processing (biomaterials and stem cell sources), processing (the 3D bioprinting systems and techniques) and post-processing (cell culture in bioreactors). During laboratory class, students will get acquainted with the additive technologies on various bioprinting installations, biomimetics topological design and biomaterial part mechanical testing. |
Igor Shishkovsky | 3 | MA030354 | ||
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Academic Communication: Preparatory English for PhD Exam (Term 3-4)
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities to be performed in class and at home, individually and in teams. By the end of the course, successful participants will |
Elizaveta Tikhomirova |
31.5 per term
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Extra | DF030029 | |
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Advanced Additive Manufacturing – Ceramics
The course is focused on understanding of photopolymerization-based (SLA) additive manufacturing (AM) technology as the perspective method of production of complex ceramic items. Different AM methods are considered in the course. Their Pros and Cons are evaluated. The physical principles governing the 3D-printing of ceramics with SLA method are presented. Basic tests of ceramic feedstock behavior with respect to different laser impact are explored in the course’s practical section. The details on ceramic items processing are unveiled. Ceramics sintering is discussed with examples of different process parameters and corresponding material outcomes. Finally, laboratory methods of ceramic material evaluation are studied.
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Svyatoslav Chugunov | 3 | MA030516 | ||
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Advanced Control Methods
This course is dedicated to modern methods of automatic control of dynamical systems.
The course offers a wide coverage of the Lyapunov stability theory of dynamical systems, including control Lyapunov functions and their nonsmooth variants. Sliding-mode control, model-predictive control, feedback linearization, backstepping (including its nonsmooth variant) are all considered. Elements of adaptive, robust and fault-tolerant control are addressed. You are offered a package of 9 lectures, 9 seminars, 4 homework and 1 final project assignment. The course is rich on Python coding of advanced controllers. |
Pavel Osinenko | 6 | MA060501 | ||
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Advanced Materials Modeling
The course builds on introductory Computational Chemistry and Materials Modeling course to provide in-depth understanding and advanced-level use of commonly employed modeling methods, as well as teach state of the art methods tailored for modeling of specific classes of materials and physical processes relevant to multiple major industries in the future, including nanotechnology and energy. The emphasis is on deep understanding and practical use of techniques, algorithms and programs to bridge theory and applications, from the discovery of materials to their use in real-world technologies. Personalized advisory of several experts in different areas of computational materials science will allow students to accomplish challenging projects related to their PhD/MSc theses or other research in materials science.
At the end of the course, students will learn advantages and limitations of various approximations in electronic-structure modelling, both within the framework of density-functional theory and beyond (including many-body perturbation theory). Methods for describing effects of external factors such as temperature, pressure, and doping on properties of materials will be also discussed. Students will also learn modern methods of artificial intelligence for materials design. During practical lessons students will learn to perform calculations of atomic and electronic structure of materials and evaluate reliability of obtained results. Students will also have an opportunity to apply in practice molecular dynamics based methods, as well as machine learning methods. Students will master electronic-structure packages Abinit and FHI-aims, as well as other programs, including advanced molecular dynamics and machine learning algorithms. |
Sergey Levchenko | 6 | DA060341 | ||
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Applied Materials and Design
This course provides a broad base introduction into materials science and engineering of applied materials. The fundamental physical phenomena are considered that occur at different scales in the main classes of applied materials: metals, ceramics, polymers, natural materials, composites, and hybrids. The interrelation between thermodynamics, diffusion kinetics, and deformation behavior is explored. The concept of structure is introduced, and the nature of structural elements at the atomic, molecular, nano-, micrometer and macroscopic scales is discussed: short and long range order in amorphous materials and crystals, defects, crystallites, grains and subgrains, precipitates, grain boundaries, interfaces, spherulites, etc. These are used to demonstrate the principal approaches to property control and evaluation in materials engineering and related technologies: chemical composition, synthesis, fabrication, heat treatment, plastic deformation, hybridization, and surface engineering.
Principles to control the properties are translated in terms of design performance. Ashby’s material selection algorithm for rational selection of materials for specific designs and applications will be taught here in comprehensive way – analysis of function, objectives and constraints, deducing of performance indices. All the concepts covered in lectures will be practiced by using CES EduPack a software to implement data intensive learning. The lectures will be supported with a number of laboratory practical lessons devoted to the development of practical skills in traditional materials science research flow – the visualization, characterization and modification of structure followed by the testing and analysis of properties. All the concepts covered in lectures will be the subject of exercises using open source software to implement data intensive learning. Individual projects (problems) will be formulated to introduce the CDIO approach in Applied Materials and Design. |
Alexander Korsunsky | 6 | MA060431 | ||
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Biomaterials and Nanomedicine
This module will define and describe biomaterials in general term with particular attention to modern concepts of nanomedicine. It will include basics in properties of Biomaterials Surfaces considering their Physics and Chemistry, Methods of surface modification and characterization, approaches to fabricate Biocompatible surface. This will follow aspects of Protein – surface and Cell-Surface interaction. Biomaterials part concludes with fabrication and use of Scaffolds, implants, eluting stents and gels for tissue engineering
Nanomedicine part includes overview of Micro- & Nanoparticles application as biomaterials, concept of Drug Delivery systems, Controlled and Triggered release Course include considering recent original and review paper by students and lab work on formation of biomaterials of certain sort and their characterisation |
Gleb Sukhorukov | 3 | MA030405 | ||
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Biomedical Application of Photonics
The overview of the current state of photonics application in biology and medicine will be presented including optical properties of a cell, biological tissue, body (absorption, reflection, scattering, fluorescence). Now the photonic tools are used for imaging, diagnostics, manipulation, therapy, and surgery at three different levels – cellular, tissue, and body, therefore the course aims to teach students to understand basic principles of the current biomedical applications of photonics tools. Every level is required to apply a different approach, for example for cellular-level imaging, manipulation, Confocal LS Microscopy (including the technology of quotative analysis as FRAP, photoconversion, FLIP, FLAP, FRET, FLIM, FCS, FCCS), darkfield microscopy; optical tweezers approach, laser cell poration; for diagnostics – Raman microscopy, CARS, in vitro and in vivo fluorescent flow cytometry, in vivo flow photoacoustic (PA) cytometry; and for therapy – laser-induced necrosis and apoptosis; in vivo flow PA setup for theranostics are used. Tissue level requires imaging, multiphoton microscopy, SHG and THG microscopy, OCT, raster-scan optoacoustic mesoscopy (RSOM); for manipulation – laser 3D printing, laser skin perforation. Body level includes for imaging – OCT, MRI, CT, MRI, fluorescence and optoacoustic imaging, US, PET; for diagnostics – different types of in vivo sensors including implantable medical devices, smart tattoo; for therapy – photodynamic and photothermal therapy; for surgery – photonic approach guided surgery including endoscopy, high speed surgery with the highest resolution. Topics also include description of different types of contrast and optical clearing agents. The course will also offer a practice in operation of imaging systems on the cell, tissue and body level such as fluorescent microscopy, RSOM, fluorescence imaging. Students will have experience related to application the most appropriate photonic tools for their own research projects.
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Dmitry Gorin | 6 | MA060158 | ||
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Biomedical Imaging and Analytics
This course is designed for Machine Learning and Data-Science students who would like to concentrate their research on the analysis of biomedical images. This cohort of specialists – especially early on in their careers – is known for dismissing both the physical mechanisms of image formation and the very biological rationale behind a given imaging modality. In this course, we will attempt to reunite the three disciplines in order to help students develop systematic analytical expertise and biological intuition.
The course is also aligned with the curriculum of the centers of life sciences and photonics and should be used for enriching those offerings with modern machine learning and image analytics skills. Students will learn what forms the backbone of biomedical imaging, drawing from the mathematical, physical, chemical, and biological sciences, including the subjects of: • Light microscopy (live cell imaging, deconvolution and superresolution microscopy, 3D microscopy, Optical Coherence Tomography); • Medical imaging (X-ray, Computed Tomography, Magnetic Resonance Imaging, Ultrasound, Positron Emission Tomography); • Image analytics (filtering and signal processing, machine learning and artificial neural networks, computer vision, image-based biological and physiological modeling). The course is offered primarily for computer science students who are assumed to know the basics of image processing and machine learning. In addition to the lectures, there will be 6 adapted seminars for those students who encounter the technical aspects of this course for the first time (e.g., Life Sciences students). The technical sessions will be shuffled with invited seminars by doctors/biologists with whom there is an ongoing collaboration. |
Dmitry Dylov | 6 | MA060305 | ||
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Biostatistics
This introductory course to statistics and probability theory is modeled as an extension of a traditional university Statistics course and Advanced Placement Course in Statistics to a broader spectrum of topics, while keeping the spirit of quantitative discourse applied to real-life problems. The material is offered in 5 consecutive modules (see Course Outline below), each containing a lecture, a discussion section, and a practicum. For practical exercises we use R programming language and R-Studio software. However, this course is focused on statistics rather than R; therefore, each practicum is designed with the purpose to demonstrate and reinforce understanding of concepts introduced in the lecture rather than to provide a training in R.
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Dmitri Pervouchine | 6 | MA060036 | ||
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Carbonate Reservoir Geology, 3D Modeling and Preparing Research Reports
The course provides theoretical knowledge in carbonate reservoir geology, practical skills in 3D geomodeling software, as well as professional language competence in writing and presenting the resulting reports and research articles in reservoir engineering domain.
The course has three main components. Theory is presented during the lectures where students will get familiar with the most important essentials of carbonate reservoir geology and gain a consistent theoretical knowledge in applied subjects required for scientifically-minded geological modeling. In computer classes, students will gain applied skills in 3D geomodeling software. During the software practicums the participants will learn the main geomodeling operations, such as creating a project for a field; import / export, displaying and handling different types of geo-data; construction of a reservoir stratigraphic and structural frameworks; facies modelling; petrophysical modeling and reserves calculation. The course also includes a module aimed at preparing applied research reports, presentations and scientific papers in English language. The module will enable students acquiring professional vocabulary, syntax and grammar needed to understand, analyze and generate industrial reports, professional papers as well as present them to international conferences. This course is the final course in the Reservoir Geology and Property Evaluation triad consisting of the courses in Reservoir Rock Characterization (and laboratory core analyses), Petrophysics and Well Log Interpretation; and Reservoir Geology and 3D Modeling. Upon completion of these courses, the students will get a comprehensive knowledge and practical skills required from a modern reservoir geologist operating hydrocarbon and geothermal reservoirs as well as subsurface energy storages. A unique course design makes it scalable for student of different specialization. |
Alexei Tchistiakov | 6 | MA060467 | ||
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Catalysis
The present course aims to provide a concise but comprehensive introduction to a multidisciplinary field of catalysis. As it affects more than 90% of the chemical industry and generates up to 30 % of the GDP, catalysis employs models and methods from a wide variety of disciplines: from physical chemistry to solid-state physics, from quantum chemistry to hydrodynamics. Thus, two strategies are usually used for teaching: separate educational programs (>2 years) or brief discussion during one or two lectures max. Here we wish to propose a third way – a 3 credit course with problem-oriented education. During the course, the students will learn basic principles and concepts of catalysis and develop a team project on a particular and demanding scientific problem. The latter will be defended during the discussion seminar with other student teams acting as opponents and reviewers.
Totally 21 hours of lectures, 9 hours of exercises and 3 hours of discussion work. During the courses each student is supposed to take part in the team project that will be concluded with a 15 min presentation. |
Dmitry Krasnikov | 6 | MA060502 | ||
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Cell Biology Lab Course
Lab course in Cell biology provides students an opportunity to explore how the techniques of molecular and cell biology may be used to understand cell function. Laboratory practice in cell biology will provide the experience in genetic manipulations with cell lines, immunostaining and fluorescence or confocal microscopy analysis. The main aspects for FACS-analysis and cell sorting will be introduced. The approaches for gene expression analysis by RT-qPCR and Western blot will be used to understand the influence of genetic manipulation to the cell function. The introduction in powerful approach to understand the protein interaction such as SPR optical biosensor will be provided.
The introduction in high-throughput screening of biologically active compounds will be provided. The course will provide students with a hands-on understanding of modern methods of cellular manipulation and understanding the mechanism of cell functioning. |
Olga Dontsova | 6 | MA060134 | ||
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Computational Materials Science Seminar (Term 3-4)
This is the main research seminar at Skoltech for Computational Materials scientists. All students of Computational Materials Science subtrack of Materials Science MSc program and Materials Science and Engineering PhD program should attend this seminar. Topics include materials modeling (at atomistic scale), theoretical and computational chemistry, theoretical and computational physics of materials, underlying mathematical methods and algorithms etc. Invited lectures are top scientists in their research field. Students are welcome to present their own research results at this seminar and expected to do this at least once per two terms.
Please see the seminar webpage at https://www.skoltech.ru/en/cms/ |
Dmitry Aksenov |
30.5 per term
|
MA030430 | ||
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Data Analysis for Space Weather
The course introduces students to Solar-Terrestrial physics, Space Weather and practically useful approaches of data analysis for study, forecasting, and mitigation of space weather effects. The course provides an overview of Sun-Earth connections, starting from the interior of the Sun and ending in the Earth's magnetosphere. To gain insight into this field, we focus on such topics as: solar interior and solar structure, solar atmosphere, solar wind, solar flares and coronal mass ejections, as well as associated geomagnetic storms and polar auroras. These phenomena drive Space Weather with the implications for space-borne and ground-based technological systems (satellites, human spaceflight, airlines, power systems and pipelines). We also examine the space weather effects on technology and human health, hazard assessment, mitigation and forecasting, space environment data, scientific and service products.
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Tatiana Podladchikova | 6 | DA060309 | ||
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Deep Learning
The course is about Deep Learning, i.e. a new generation of neural network-based methods that have dramatically improved the performance of AI systems in such domains as computer vision, speech recognition, natural language analysis, reinforcement learning, bioinformatics. The course covers the basics of supervised and unsupervised deep learning and other problem statements for training deep neural models. It also covers the details of the two most successful classes of models, namely convolutional networks and models based on the attention mechanism. In terms of application, the class emphasizes computer vision and natural language analysis tasks. The course involves a significant practical component with a large number of practical assignments.
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Ivan Oseledets | 6 | DA060057 | ||
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Deep Learning for Natural Language Processing
The course is about modern models for natural language processing. The new generation of neural network-based methods based on deep learning has dramatically improved the performance of a wide range of natural language processing tasks, ranging from text classification to question answering. The course covers the basics and the details of successful models and methods for natural language processing based on neural networks, starting from the simple word embedding models, such as word2vec, all the way to more sophisticated language models, such BERT. Special attention is given to models based on the Transformer architecture, such as GPT, T5, BART, etc.
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Alexander Panchenko | 3 | MA030556 | ||
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Design of Chemical Sensors: from Fundamentals to Applications
The course is devoted to the development of chemical sensors and the assessment of their analytical figures of merit mainly from a viewpoint of material science with an emphasis on functional nanomaterials.
The sensors are about signal transduction of a chemical interaction to a physical signal which can be measured or assessed by a human. As most processes, which are intended to be a basis for sensor development, occur on the surface, they involve physical and chemical phenomena. Thus, it is important to understand such a physico-chemical system as a whole that leads to selective, sensitive recognition of specific interaction. In our course, we will examine and go from the basics of surface physics and chemistry to the real applications of materials and nanomaterials in chemical sensors and analytical systems. We will study the operation principle of the most widely applied sensors like resistive, electrochemical, acoustic, mass, biosensors, and optical ones. The synthesis of sensitive materials and the design of chemical sensors will also be considered. We will finally touch on the multisensor system and data analysis, including the machine learning protocols. |
Fedor Fedorov | 3 | MA030446 | ||
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Developing Products and Services Through Design Thinking
The complexity and uncertainty of the world of business require a set of skills that combine analytical approaches with creative ones. The challenges are more often unstable, unpredictable, and complex. To be competitive in this environment, specialists should be able to combine analytical and creative approaches.
During the course, we will follow the Design Thinking approach to tackle an innovation challenge. The course is set-up like a workshop, where teams work on their challenge on a weekly basis receiving lectures that introduce practical methods that can soon be put into practice. Design Thinking is a process that iteratively seeks to understand user needs, challenge well-established assumptions, and redefine problems. |
Alexander Chekanov | 3 | E&I | MC030022 | |
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Digital Technologies in Electrical Grids
The course first gives the students an overview of the process and goals of the digital transformation, with the focus on power grids. Next, the students learn about the structure of power grid companies in Russia, their problems, development strategy, and motivation to participate in the digital transformation.
There will also be guest lectures on different experiences related to commercialization of innovation in Russian power grid companies. However, the key course outcome is practical – to give the students necessary skills so that they will be able to commercialize their technology with their industrial partner (a power grid company). The students will obtain these skills while working on their projects throughout the course. The project goal is to develop a commercialization proposal for the technology of the student’s Master thesis. This proposal in the form of the final presentation will receive feedback from the industrial jury and the rest of the students. The project involves several major components: – defining the partner/customer, – analyzing the partner development strategy, – defining the customer problem and its solution with the target technology, – articulating the product/project value proposition, – specifying the product/project requirements, – assessing economic effects and risks of the product/project implementation, – selecting the form of the innovation commercialization. The students will be progressing with their projects by doing seminar and homework tasks. An important step will be conducting three product interviews with the industry representatives – done individually by each of the students/teams. The purpose of these interviews is to validate the student’s hypotheses on the customer problem, value proposition, etc. |
Dmitry Titov | 3 | MA030476 | ||
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Electrochemistry: Fundamentals to Applications
This course covers fundamental concepts of electrochemistry: oxidation and reduction processes, types of conductors, electrolytes, classification of electrodes and electrode reactions, Faraday’s Laws, and electroanalytical methods. In addition, some applied aspects of electrochemistry will be covered including industrial electrolytic processes, electrodeposition, and electrochemical power sources (batteries and fuel cells).
The prerequisites are: undergraduate math, chemistry, and physics. |
Victoria Nikitina | 6 | MA060127 | MOVED FROM T4/8 | |
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Embedded Systems and Intelligent Sensors
This module will give a wide-ranging introduction to sensors and embedded systems in the scope of Internet of Things (IoT) paradigm. The module aims at providing full support to the non-engineering students with a series of carefully constructed concepts and exercises. It starts with setting the whole picture of IoT and its requirements for sensors and embedded systems. Then it introduces basic principles and simple projects, and moves towards more advanced IoT system design. Finally, the module will make overview of targeted applications including Smart-X, Oil & Gas industry, wearables and medical applications.
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Andrey Somov | 6 | MA060474 | ||
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English. Candidate Examination
This is a blended meta-course for the English Qualification Exam needed for the Russian PhD Degree. The Exam is designed as a multidisciplinary conference where the participants present results of their PhD research structured according to the general principles of conference materials submission, peer review, resubmission, presentation, and discussion.
The goal of the Exam is Academic Communication, so the participants should demonstrate the ability to present their research results in front of a multidisciplinary audience and deliver the key ideas in good Academic English in terms of vocabulary, grammar and style. Pre-exam/ pre-conference activities, such as material submissions and peer reviews, take place fully online. They include: Project proposal V1+ 2 Peer Reviews; a 2-minute video annotation V1 + peer review; and a stack of presentation slides V1+ peer review. Version 2 of the Proposal, video annotation and the slides should be improved using the comments of the Instructor and the peers. Depending on the applicable regulations related to COVID-19, on the Examination day students make their presentations and participate in the discussion in person or via an online platform in front of the Examination Committee and a group of peers. Failure to submit an assignment by the due date may result in the loss of the grade. The participants will practice a variety of academic skills: – Planning and designing a well-structured and balanced presentation The grade is counted towards the PhD Qualification. |
Elizaveta Tikhomirova | 3 | DG030003 | ||
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Evolutionary Neurobiology
Evolutionary neurobiology course will focus on the molecular mechanisms of human brain evolution and associated social and behavioral aspects. The course will be structured as a lecture-seminar series with a topic first presented at a lecture and then discussed on a seminar. Topics will include different aspects the human brain evolution pertaining its organization and behavior. More specifically, we will discuss genetic, functional genomics, anatomical, and behavioral studies of the humans and their closest mammalian relatives. We will also discuss some of the psychiatric disorders affecting human brain functions and whether and how studies of these disorders shed light on the evolution of human-specific cognitive abilities. As a result of this course students should be able to comprehend and critically assess the literature on the subject of human cognition and human brain evolution.
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Philipp Khaitovich | 3 | MA030551 | ||
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Experimental Optics II
Course description: The experimental optics course focuses on providing students with the basic practical skills required to kick-start a successful photonics career. The course is centred around four experimental projects that are relevant to different areas of photonics: single photon counting (demonstrating the particle nature of light), holography (demonstrating the wave nature of light), polarization, a very important property of light and introduction to fundamental measurement techniques. The course includes a general introduction to lasers and optoelectronics and regular lectures which are relevant to the specific topics of the experiment. The students will work semi-independently in small group of 2-3 students with possible supervision from a teaching assistant.
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Sakellaris Mailis | 6 | MA060336 | ||
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Fabrication Technology of Nanodevices
The course concerns the fundamental and practical aspects of fabrication technologies widely used for the fabrication of nanoscale devices. The course starts with the introduction of cleanroom environment, code of practise and safety for operation in Nanofabrication centres. There are discussed a range of technologies and methods: UV and Electron Beam lithographies, wet and dry etching, thin film deposition, thermal annealing, controllable oxidation and ion beam implantation, metrology of nanoscale devices. An introduction to chemicals used for fabrication and safety operation is given. Finally, examples of fabrication of devices are discussed. Students will have a chance to learn practical operation on some equipment.
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Vladimir Antonov | 6 | MA060311 | ||
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Functional Materials and Coatings
Functional materials play an important role in fields such as engineering, medicine, and space applications. Functional materials are a group of engineered and advanced materials designed and synthesized for some specific function and tailored properties. The structure of solids, phase transformations and relations between structure and functional properties will be discussed during the first part of the lectures. Material’s classes that will be presented are structural materials, metal matrix composites and intermetallic, ceramics, carbon nanotubes and graphene like structures. In the second part, much of attention will be given to surface engineering techniques with emphasis to thermal spray coatings technology to obtain functional materials. In the third part, the students will be involved in practical oriented activities to gain and master the hands-on experience in laboratory environment with importance to characterization and properties evaluation of materials.
The course is based on lectures, exercises and a mandatory project work in the laboratory. Students’ key learning objectives are: – Develop scientific knowledge and hands-on skills; – Identify functional properties of materials required for engineering applications; – Able to propose materials suitable for the application; – Perform properties evaluations of functional materials and assessing structural features using characterization methods and analysis tools; – Build finite element models and perform computational fluid dynamic simulation for coating deposition process. |
Dmitriy Dzhurinskiy | 6 | MA060514 | ||
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Fundamentals of Fluid Mechanics
The course is an introduction to the foundations of modern fluid dynamics emphasizing fundamental results on compressible and incompressible flows, viscous and inviscid flows, free-surface flows, capillary phenomena, flow instabilities, and shock waves. The course begins with the derivation of general equations of motion of continua, introduces Lagrangian and Eulerian representations, material derivative, Reynolds transport theorem, conservation of mass and momentum, Cauchy’s stress principle, Cauchy’s lemma, conservation of angular momentum, kinetic energy equation, thermodynamic considerations, entropy inequality. Introduction to viscous fluids, Stokesian fluids, Rivlin-Ericksen theorem, Newtonian fluids, Navier-Stokes equations. Compressible vs incompressible Navier-Stokes equations. Ideal fluids, Bernoulli’s theorem, vorticity, vortex lines, vortex sheets and tubes, vortex stretching, material vector fields, theorems by Kelvin and Helmholtz. Potential flows, use of complex variables, D’Alembert’s theorem. Point vortices, vortex patterns, Hamiltonian structure of point-vortex dynamics. Free-surface problems, surface tension, static problems with surface tension. Flow of thin films, lubrication theory. Basic viscous flow problems. Boundary layers. Basic problems of hydrodynamic stability: Kelvin-Helmholtz, Rayleigh-Taylor, Rayleigh-Plateau instabilities, stability of parallel flows. Introduction to compressible flows and shock waves.
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Aslan Kasimov | 3 | MA030570 | ||
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Fundamentals of Post-Quantum Cryptography
In this course, we will cover modern techniques of post-quantum cryptography that become more and more popular due to recent advances in quantum-computers and quantum algorithms for solving classical mathematical problems forming the basis of current cryptography-techniques. For example, the problem of big number factorization on what RSA is based can be solved by the Shor algorithm. The course will be divided into three main parts:
1. Classical cryptography |
Grigory Kabatyansky | 3 | MA030408 | ||
| Fundamentals of Solid Mechanics | Ghader Rezazadeh | 3 | MA030575 | ||
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Geometrical Methods of Machine Learning
Many machine learning problems are fundamentally geometric in nature. The general goal of machine learning is to extract previously unknown information from data, which is reflected in the structure (underlying geometry) of the data. Thus, understanding the shape of the high-dimensional data plays an important role in modern learning theory and data analytics.
Real-world data obtained from natural sources occupy usually only a very small part of the ‘observation’ space and concentrate non-uniformly along lower dimensional structures, and geometrical methods allow discovering the shape of these structures from given data. A large part of the course addresses to most popular geometrical model of high-dimensional data called manifold model and introduces modern manifold learning methods. The course includes also Topological Data Analysis that is an emerging trend in exploratory data analysis and data mining and provide a general framework (a set of topological and geometric tools) to analyze high-dimensional, incomplete and noisy data. Necessary short information on differential geometry and topology will be given in the course. The course will provide examples of the application of geometric and topological methods of data analysis to various applied problems. The course is useful for MSc and PhD students interested in recent geometrical methods, lying at the interface between Mathematics and Machine learning. At the end of the course, students will know the basic ideas of a geometric approach to data analysis, possess modern geometric and topological methods of data analysis and be able to apply them to solve basic machine learning problems, such as classification, regression, dimensionality reduction, data presentation and visualization, clustering and others. This knowledge and skills allows them to participate in real-life projects to solve complex applied problems of data analysis. |
Alexander Bernstein | 3 | MA030169 | ||
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Geostatistics and Reservoir Simulation
The course includes lectures in reservoir simulation, history matching, and fundamentals of geostatistics.
Reservoir simulation and history matching embrace the following: 1) Fundamentals of single-phase and multiphase multicomponent fluid flow and storage in reservoirs 2) Numerical solution of governing equations using finite difference 3) Introduction to inverse theory and history matching 4) Simulating of laboratory PVT data by Equation Of States (EOS) Fundamentals of geostatistics include the following: 1) Stochastic reservoir simulation 2) Statistical measures 3) Univariate and multivariate Statistics 4) Covariance and variograms 5) Sequential Gaussian simulation 6) Uncertainty quantification Finally, reservoir simulation and geostatistical analysis are integrated for risk analysis and economy estimation. Laboratory computational exercises are also included. |
Dmitri Koroteev | 6 | MA060085 | ||
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High Performance Computing and Modern Architectures
High Performance Computing refers to accumulation and usage of computing power well beyond a typical desktop or laptop computer. This is a main course for various aspects of HPC and a further development of the Scientific Computing course. Together with the theoretical part and discussion of basic parallel algorithms, the course will have a practical component aimed at solving different research and industry-related problems on different computing architectures, such as modern CPUs and GPUs. The course will provide sufficient knowledge and experience in using standard parallel libraries (such as OpenMP, MPI and CUDA) as well as visualization software (ParaView, Visit). Students will be given a chance of using Skoltech's world-class HPC facilities to learn typical methods and rules of working on the large-scale collectively used supercomputers. The course is designed in such a way that students who successfully pass the exam will be able to use advanced methods of HPC in their everyday work.
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Sergey Rykovanov | 6 | MA060287 | ||
| Innovators’ Essential Skills: Critical and Creative Thinking, Communication and Collaboration | Maxim Kiselev | 3 | E&I | MC030564 | |
| Integrable Many-Body Systems and Nonlinear Equations (Term 3-4) | Anton Zabrodin |
63 per term
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MA060602 | ||
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Introduction to Quantum Field Theory (Term 3-4)
As you know, the modern theory of fundamental physics (the "standard model of elementary particle physics") is a quantum field theory (QFT). In addition to this central role in modern physics, quantum field theory also has many applications in pure mathematics (for example, from it came the so-called quantum knot invariants and Gromov-Witten invariants of symplectic manifolds).
"Ordinary" quantum mechanics deals with systems with a fixed number of particles. In QFP, the objects of study are fields (not in the sense of a "field of complex numbers", but in the sense of an "electromagnetic field"), whose elementary perturbations are analogs of quantum mechanical particles, but can appear and disappear ("born" and "die"); at the same time, the number of degrees of freedom turns out to be infinite. Within the framework of this course, the basic concepts of QFT will be introduced "from scratch". The Fock space and the formalism of operators on it, as well as the formalism of the "continuum integral" will be defined. The main example under consideration will be the quantum scalar field theory. A scalar field in physical terminology is a field that, at the classical level, is defined by one number at each point (i.e., in fact, its state at a given time is just a numerical function on space), unlike a vector field (an example of which, in particular, is an electromagnetic field). However, considering the quantum theory of a scalar field (even separately, and simpler than for the Higgs field) is in any case very useful, since it allows you to get acquainted with the apparatus and phenomena of QFT on a simpler example than vector and spinor fields. The course will consider the "perturbation theory" (that is, in fact, a method for calculating the first orders of smallness in a small parameter expansion) for a scalar field and describe ways to calculate various probabilities of events with particles. |
Vladimir Losyakov |
63 per term
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MA060505 | ||
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Machine Learning for Engineering Applications
The course is aimed at studying and applying machine learning methods for solving research and engineering problems. The course consists of two parts. The first part of the course will explain the main methods of case learning: classification, clustering, regression, dimensionality reduction. Unsupervised neural networks and elements of attention models and transformers will also be considered. In the second part of the course, applied problems of machine learning will be discussed: automatic search for defects in a material, finding promising materials with desired properties, predicting material properties, adaptive design of materials, improving images of a transmission electron microscope.
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Petr Zhilyaev | 3 | MA030518 | ||
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Machine Learning in Structural Bioinformatics and Chemoinformatics
Deep learning achieves remarkable results in many fields, including life sciences. This course is made for students, that ready to apply their skills to scientific applications, such as biomolecular design, analysis, and drug discovery. Molecules play vital roles in our organism, constantly interacting with each other and serving all the functionality, that we have as human beings. Prediction of molecular properties, as well as the design of molecules with target properties, are highly important problems, that still need to be addressed. Complex and rich nature of molecules allows us to represent them as sequences, graphs, 3D objects, or high-dimensional descriptors, and to apply numerical methods in order to solve open problems in structural bioinformatics and chemoinformatics. Rapid accumulation of molecular data opened gates for machine learning to be applied for such representations and to derive powerful prediction models that outperform heuristical methods.
During this course, students will be introduced to open problems in structural bioinformatics and chemoinformatics and with state-of-the-art machine learning methods attempt to solve these problems. Particularly students will practice machine learning for drug design and biomolecular analysis problems. The course includes theoretical lectures, for which basic knowledge of structural chemistry and biology is required. Seminars are python coding sessions, where students develop and apply machine learning pipelines to derive prediction models. The end of the course comprises the capstone project aimed to solve the structural bioinformatics and chemoinformatics problem of choice using machine learning. |
Petr Popov | 6 | MA060471 | ||
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Mathematical Modelling in Innovation
The discipline and practice of mathematics forms the very foundation of most modern engineering sciences, however, the gap between mathematical skills and entrepreneurial skills is immense. End users usually do not understand and can not value the impact of mathematics on solving their everyday problems. Mathematiciapns usually do not see and can not grasp the path from their academic exercises to the real life problems and their solutions. In this course we will build bridges between purely academic math tasks and everyday problems of the society. To that end we will will stratify the industries and solutions most relying on the complex math and then will build our own prototypes of innovative solutions with embedded complex mathematics.
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Dmitry Kulish | 6 | E&I | DC060021 | |
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Methods of Enhanced Oil Recovery
Over one-half of the original oil-in-place remains in the reservoirs as primary and secondary recovery techniques have its technological and economic limitations. Major reasons are:
• heterogeneity of the reservoirs; • unfavorable fluid properties; • inefficient nature of the displacement process; economic constraints. A better understanding of the reservoir fundamentals and the important variables that influence the recovery process can enhance oil recovery. This course presents a comprehensive summary of chemical, miscible, and thermal enhanced oil recovery processes. The course presents the subject material with a clear focus on developing and producing the reservoir efficiently, energies available within the reservoir, realizing technical benefits and application limitations of the various enhanced oil recovery methods. |
Alexey Cheremisin | 6 | MA060117 | ||
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Modern Applications of Information Theory
The aim of the course is to explain modern ideas and concepts of information theory, as well as to present the emerging use-cases.
The main focus of the course are the topics on the border of information theory, communications and machine learning, in particular (a) graphical models and iterative decoding; (b) deep neural network (DNN) based data compression; (c) DNN-based channel decoding; (d) information theory based analysis of DNNs and information bottleneck; (e) Massive random access in 5G/6G and interconnection to the compressive sensing problem. |
Alexey Frolov, Grigory Kabatyansky |
3 | MA030414 | ||
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Modern Dynamical Systems (Term 3-4)
Dynamical systems in our course will be presented mainly not as an independent branch of mathematics but as a very powerful tool that can be applied in geometry, topology, probability, analysis, number theory and physics. We consciously decided to sacrifice some classical chapters of ergodic theory and to introduce the most important dynamical notions and ideas in the geometric and topological context already intuitively familiar to our audience. As a compensation, we will show applications of dynamics to important problems in other mathematical disciplines. We hope to arrive at the end of the course to the most recent advances in dynamics and geometry and to present (at least informally) some of results of A. Avila, A. Eskin, M. Kontsevich, M. Mirzakhani, G. Margulis.
In accordance with this strategy, the course comprises several blocks closely related to each other. The first three of them (including very short introduction) are mainly mandatory. The decision, which of the topics listed below these three blocks would depend on the background and interests of the audience. |
Alexandra Skripchenko |
63 per term
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MA060257 | ||
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Modern Plant Breeding Workshop
This teaching unit is built around "The Plant Breeding Project", a common thread throughout the course carried out by 'plant breeders teams' (typically 3-4 students). This project focuses on the genetic improvement of a crop for agronomic traits of interest. The goal is to propose a strategy for improving the species for the considered traits and to discuss the possible alternatives.
To do so, the students will analyse real large-scale phenotypic and genomic data sets while implementing suitable and detailed bio-statistical and quantitative genetic methods and relying on scientific articles presenting the most up-to-date and groundbreaking AgBioTechs available for the studied crop. "Resources" lectures and hands-on will provide the knowledge and skills needed for carrying out the project. These sessions will survey the most suitable methods available to plant breeders, from the evaluation of the genetic values of genotypes to classical breeding schemes, and Marker- and Genomic-Assisted Selection (MAS, genomic selection). Genotype X Environment X Management (GXEXM) interaction, landscape genomics, envirotyping, and other prospective approaches in plant breeding such as high-throughput (HT) phenomics, plant breeding digitalization, will also be studied… Creativity sessions using design thinking methods (e.g. group brainstorming) will make it possible to reflect on the challenges, needs, and avenues of innovation for the crop and agri-sector concerned, as well as on the definition of key selection targets, strategy and possible alternatives that can be implemented for a program to improve the plant species considered. This course is implemented on a skill-based learning and active pedagogy approach where each concept and method is illustrated during hands-on on practical real case studies, tutored team projects or reversed classes. It trains executives specialized in plant breeding and the creation of plant varieties. |
Cecile Ben | 6 | MA060510 | ||
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Nanomaterials E&I
The present course aims to join multidisciplinary fields of Science, Technology and Innovation to efficiently utilize novel material technologies for creating innovative products and services under the framework of E&I. The course starts with the theoretical description of the various nanomaterials and nanotechnological approaches with focus on carbon nanomaterials that are hallmarks of Prof. Nasibulin lab scientific work. In the second part of the course students will synthesize and handle some of the nanomaterials and investigate their properties. In the third part of the course students will be guided by Prof. Kulish team into the intensive E&I product development, comprised by two cycles of CusDev and prototyping based on the technologies studied in the early stages of the course. As a result, students will go through an exciting journey from deep tech science to real startups.
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Albert Nasibulin, Dmitry Kulish |
6 | E&I | MC060030 | |
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Nanomaterials E&I
The present course aims to join multidisciplinary fields of Science, Technology and Innovation to efficiently utilize novel material technologies for creating innovative products and services under the framework of E&I. The course starts with the theoretical description of the various nanomaterials and nanotechnological approaches with focus on carbon nanomaterials that are hallmarks of Prof. Nasibulin lab scientific work. In the second part of the course students will synthesize and handle some of the nanomaterials and investigate their properties. In the third part of the course students will be guided by Prof. Kulish team into the intensive E&I product development, comprised by two cycles of CusDev and prototyping based on the technologies studied in the early stages of the course. As a result, students will go through an exciting journey from deep tech science to real startups.
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Albert Nasibulin, Dmitry Kulish |
3 | E&I | MC030030 | CANCELLED |
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Neuromorphic Computing
Over the past decades the concept of neuromorphic computing that relies on imitating and exploiting the mechanisms inherent to biological nervous system has evolved into an interdisciplinary research area at the boundary between advanced computing and computational neuroscience. This direction is largely considered as one of the most promising approach to resolve the critical problems that come with continual miniaturization and ever-increasing power consumption of CMOS technology. A vast number of brain-inspired algorithms and architectures, endowing low-power requirements and massive parallel computing principles, have been attempted for applications, including complex pattern recognition, image processing, and data mining. In parallel, intensive research has been conducted for practical implementation of learning-based artificial synapses and neurons that represent two fundamental building blocks of biological neural networks.
The course is designed to provide the students with basic understanding and familiarize them with recent achievements in the field of neuromorphic engineering as implemented in artificial and spiking neural networks. In the course we will address: (i) Mathematical modeling of neurons with synapses and cognitive processes; (ii) Artificial neural networks and spiking neural networks; (iii) Temporal encoding and learning in spiking neural networks; (iv) Overview of available hardware architectures for neuromorphic computing; (v) Memristor-based neuromorphic computing. |
Dmitry Yudin | 3 | MA030407 | ||
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Neurophysiology and Neurotechnologies
Neurophysiology deals with neuronal mechanisms that govern various functions of our bodies, from motor control and somatosensory sensations to cognition. With the development of new methods, such as neural recording techniques, decoding algorithms and robotics, neurotechnologies have started to emerge that have practical significance as treatment and rehabilitation approaches to severe neural conditions. Thus, brain-computer interfaces decode brain activity and link it to assistive devices, such as prosthetic limbs and communication systems. Given these developments, educating a new generation of experts in neural technologies has become important for the advancement of science, technology and medicine, with a considerable economical impact. In this course, students will start with learning key neurophysiological concepts. They will then learn how these concepts are applied to the development of modern neurotechnologies for clinical applications and the applications for augmenting brain functions in healthy people. Course prerequisites include very basic knowledge of physiology, neurophysiology and mathematics at an undergraduate level.
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Daria Kleeva | 6 | MA060488 | ||
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Numerical Methods for Conservation Laws (Term 3-4)
The course is an introduction to the mathematical theory and numerical analysis of conservation laws that arise in applied sciences. It begins with a theory of conservation laws, which makes a first of two main parts of the course. Origin of conservation and balance laws in mechanics, physics, chemistry, biology, and other fields of science is explained. Then a theory of scalar conservation laws is developed with an introduction of such concepts as: hyperbolicity, weak solutions, shock waves and rarefactions, entropy conditions. Important nonlinear systems such as Euler equations of gasdynamics, shallow water equations, two-phase flow models, traffic flow models are analyzed. Theory of linear hyperbolic systems is given, including the solution to the Riemann problem. Then nonlinear systems are analyzed in terms of the existence and properties of shock waves, rarefactions, and the solution of the Riemann problem, including one for the 1D Euler equations of gasdynamics. The second part introduces numerical methods for conservation laws: methods for linear systems, stability, the Lax equivalence theorem, upwinding, modified equations, conservative methods, the Lax-Wendroff theorem, entropy condition, Godunov's method, approximate Riemann solvers, nonlinear stability, TVD methods, monotone methods, high-resolution methods, flux limiters, slope limiters, semi-discrete methods, ENO/WENO methods, multi-dimensional problems.
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Aslan Kasimov |
63 per term
|
MA060574 | ||
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Omics Data Analysis (Term 3-4)
An avalanche of ‘omics data is coming from different sources: transcriptomics, epigenomics, lipidomics, metabolomics. A thorough analysis of such large-scale biological data sets can lead to the discovery of important biological insights on mechanisms of cell and organ functioning, and to the identification of small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions. The course will discuss general concepts of bioinformatics analysis of various types of ‘omics data and will provide examples of their successful application for solving a wide range of biological problems. The students will not only learn the best analysis practices suitable for each data type but, importantly, understand why each analysis step is necessary, what is the logic of the whole data analysis concept, which controls are essential, etc. The course will end with the task (Final Project) on the integration of different data types produced to answer the same biological question.
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Ekaterina Khrameeva |
63 per term
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MA060061 | ||
| Omics Technology | Evgeny Nikolaev | 3 | MA030586 | ||
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Optical communications. Applications
This course is the second part of lectures devoted to modern optical communication systems. The aim of the
course is to study the applications of modern optical communication systems.The main factors limiting the capacity and distance of the transmitted signal will be considered. In particular, the linear and nonlinear impairments affecting the parameters of the propagating signal will be analyzed. The course will also consider modern issues of physics and technology of information transmission systems, including quantum methods to ensure the confidentiality of the transmitted signal, applications of nanophotonics and photonic integrated circuits, neural optical networks, radio photonics, as well as the role of photonics in the development of mobile networks of the fifth and sixth generations. |
Arkady Shipulin | 3 | MA030503 | ||
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Organic Geochemistry of Petroleum Systems
The course offers an introduction to the principles and methods of organic geochemistry as applied to conventional and unconventional hydrocarbon systems.
The course covers: the basic chemistry and biogeochemical processes involved in the generation and transformation of organic matter to different types of fossil fuels; the depositional environments that lead to the formation of effective source rocks; the use of individual organic compounds (biomarkers) and their isotopes for exploration and development of hydrocarbon deposits. Specific examples, from published literature, of applying biomarker and their stable isotope compositions in petroleum basin studies will be discussed and evaluated. Lectures are supplemented by two seminars, which will allow students to explore the role of organic matter chemical composition and the thermal history of a sedimentary basin on source rock hydrocarbon potential. Two laboratory sessions will give students hands on experience with analytical chemical procedures for organic matter analysis. A distinctive new feature of the course is the discussion of the latest analytical and instrumental techniques for molecular and stable isotope compound-specific characterization of biomarkers from source rocks and petroleum. |
Nikolai Pedentchouk | 3 | MA030466 | ||
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course describes the intended learning outcomes and how they are assessed.
The main bulk of the 81 hours of the course is spent in the actual courses in which the PhD-students do their TA-assignments. The assignments in the course itself include TA proposal and TA report and for cohorts 2022-2025 participation in training: Towards excellence in Teaching Assistantship”. Educational department should approve all assignments in Canvas in terms of content and formal requirements respectively. |
Dmitry Artamonov | 3 | DG030005 | ||
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Petroleum Geophysics
The main objective of the course is to give an overview of the main geophysical methods used for different industrial applications with the main emphasize of the cases of petroleum and geothermal exploration. The course includes a brief theoretical background of each method, general workflow of data acquisition and processing, examples of case studies from recent scientific articles and possible applications in industry. The course starts with the descriptions of the main targets of geophysical surveys consisting in mapping the shapes of underground geological structures and determination of physical properties of rocks (density, susceptibility, resistivity, and seismic wave velocities). The general principles of the forward and inverse problem solutions are presented. The course covers three general groups of geophysical methods based on 1)seismic, 2)electromagnetic and 3)natural field data.
Within the seismic part, we will consider different aspects of reflection seismic exploration, including the Common-Deep-Point standard workflow, migration problems, AVO analysis etc. This part also includes the methods based of diffracted, scattered, refracted and head wave data analysis. Passive source seismic surveys and seismic interferometry methods are also presented in this part of the course. Within the electromagnetic part, we consider a broad variety of different methods and techniques, such as vertical electrical sounding, electrical resistivity tomography, inductivity methods, georadar technology, transient and time-domain methods, and magnetotelluric sounding. This part of the course includes a variety of examples of practical applications of each method in petroleum exploration and other industrial fields. Natural field part includes multiscale studies based on the gravity and magnetic fields. We also consider existing methods for the heat flow assessment. Finally, measurements of ground deformations based on the leveling, GPS and InSAR data are presented. |
Ivan Kulakov | 6 | MA060561 | MOVED FROM TERM 3 | |
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Photonics Research Seminar Series (Term 1B-4)
The course "Photonics Research Seminar Series" aims to provide MSc and PhDs with a broader perspective into state-of-the-art research in photonics, from fundamental insights (both theoretical and experimental) to engineering and applications. The course also aims to train students in how to structure scientific results, how to provide a coherent narrative and how to appropriately present it in the form of a strictly-timed oral presentation. The bulk of the course consists of a weekly one hour seminar on seminal topics of photonics, from guest speakers or Skoltech faculty, researchers and students.
The course is a prerequisite for the research thesis defense of projects of the following laboratories (and is as such compulsory only for the 2nd year MSc students of these laboratories): |
Pavlos Lagoudakis |
30.75 per term
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MA030553 | ||
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PLM 2 - Digital Technologies in Design and Testing of Complex Technical Systems
This course is dedicated to the end-to-end design methodology, based on the PLM approach. During the course students will develop complex technical system with a modern approach to design.
The design includes: concept development, conceptual design, systems engineering, 3D physical simulation (CFD and FEM), parametric and topology optimization, final solid design. Educational process is focused on teamwork in this course. Siemens Teamcenter PLM platform is used to provide interaction within students workgroup. The course provides students with a theoretical and practical basis for implementing projects devoted to the design of complex technical systems, such as unmanned aerial vehicles. |
Clement Fortin | 6 | MA060534 | CANCELLED | |
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PLM 2 - Digital Technologies in Design and Testing of Complex Technical Systems (Term 4-5)
This course is dedicated to the end-to-end design methodology, based on the PLM approach. During the course students will develop complex technical system with a modern approach to design.
The design includes: concept development, conceptual design, systems engineering, 3D physical simulation (CFD and FEM), parametric and topology optimization, final solid design. Educational process is focused on teamwork in this course. Siemens Teamcenter PLM platform is used to provide interaction within students workgroup. The course provides students with a theoretical and practical basis for implementing projects devoted to the design of complex technical systems, such as unmanned aerial vehicles. |
Clement Fortin |
63 per term
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MA060534 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Nikolay Brilliantov | 3 | DD030020cd | ||
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Qualifying Exam: Mathematics and Mechanics
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Andrey Marshakov, Robert Nigmatulin |
3 | DD030020mm | ||
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Qualifying Exam: Petroleum Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Mikhail Spasennykh | 3 | DD030020pe | ||
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Quantum Optics
I invite you to the introductory course of Quantum Optics, which will help you catch a glimpse of the kingdom full of wonders where the electromagnetic field communicates with matter. It is the world where all motions and events occur on extremely small spatial scales. You will learn that things that are million times smaller than us are not just invisible to a naked eye but are absolutely amazing if made "visible". Quantum optics is the world of individual quanta of light, known as photons, and material quantum particles that are commonly atoms and molecules and sometimes bigger formations of those. We are going to study how photons are absorbed and emitted by the particles and how they manifest particle-like properties on their own. The fundamental importance of Quantum Optics relates to the foundations of quantum mechanics and the non-classical effects such as quantum interference and entanglement, photon antibunching and squeezing. It paves the way to numerous applications in precise measurements, protected information transfer, etc.
This introductory course includes the following topics: quantization of electromagnetic field, Fock (number) states of the field, Casimir effect, coherent and nonclassical states of light (squeezed, “Schroedinger’s Cat”, etc.), interaction of photons with atoms, Rabi and Jaynes – Cummings models. Dressed states. Dicke super- and subradiation. Quantum coherence and correlation measurements, quantum-mechanical detector of photons, quantum beam splitter, Hanbury-Brown-Twiss interferometer, bunching and antibunching of photons. Entanglement, entropy, density matrix of an atomic subsystem |
Maxim Gladush | 3 | MA030161 | ||
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Research Methodology: Bioinformatics
The course consists of two main components: discussion of typical approaches and problems common to different areas of bioinformatics and work with bioinformatics texts, such as writing papers, responding to reviewer comments, reviewing papers, writing grant proposals, etc. Both parts will be based on actual cases; in the first stream the students will analyze particular problems and apply generic rules (or spot inconsistencies in application of these rules), in the second stream the students will review actual papers and analyze reviews by others, write and review grant proposals, analyze published retractions and the reasons for them, and so on. In particular, we will systematically analyze bioinformatics papers on SARS-CoV-2.
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Mikhail Gelfand | 3 | DA030404 | ||
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Research Methodology: CMT Research Seminar (Term 2-4)
This is the main scientific seminar for the Skoltech Center for Materials Technologies (CMT). All MSc students either enrolled into the Master Program in Advanced Manufacturing Technologies or PhD students affiliated with CMT should attend this seminar. The format of the seminar is weekly invited lectures from top scientists in the research fields related to Advanced Manufacturing, Digital Engineering Technologies, and Mechanics and Physics of Advanced Manufacturing. The seminar is held in hybrid online/offline format.
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Ivan Sergeichev |
31 per term
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DG030102dm | ||
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Research Methodology: Computational and Data Science and Engineering
This course is designed to equip modern researchers with a variety of skills needed to conduct research effectively. In addition to advanced research skills and an understanding of the research environment in their fields, researchers must also have competencies in managing research-related business processes, maintaining personal effectiveness, communicating and presenting effectively, building productive professional relationships, and managing careers development effectively. Thanks to the active interaction between the instructors and students, this course comprehensively covers all these important topics.
At the end of each session, the student will be required to write a short essay in the form of answers to questions on the session's topic. At the end of the course, each student will be required to write a Final Project in the form of a Grant Project Proposal on the topic of their PhD thesis to demonstrate their mastery of the course concepts. |
Nikolay Brilliantov | 3 | DG030102 | ||
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Research Methodology: Molecular Biology
We will run an NIH-type grant panel. The students will select several (no less than 3) grant applications on different areas of molecular biology/biochemistry, provide written reviews for each one following the NIH report template and will then discuss each grant in turn (a single grant will be reviewed by three reviewers). The rest of the panel members will ask questions and the panel discussion will be recorded as a summary by a scribe (different for each). In the end all grants will be scored and funding decisions will be taken by the panel (and compared to how the grants fared in real life).
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Natalia Strushkevich | 3 | DA030403 | ||
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Research Practice Seminar "Energy Systems and Technologies" (Term 2-4)
This research seminar is the general meeting for faculty, researchers and master and PhD students of Energy Systems programs. The seminar takes place every week during Terms 2(6)-3(7)-4(8).
Master students must attend the seminar at least for one academic year but welcome to attend during two years. PhD students are welcome to attend the seminar during all years of studies but can gain no more than 6 credits in total. The seminar consists of faculty lectures, invited lectures of top scientists in their research field as well as students’ reports on their own or examined papers. To PASS the course and gain 3 credits per academic year the student must fulfill all three requirements: 1. Attendance: > 2/3 of seminars. 2. Presentation. Depending on the status: 3. Active participation in the discussion. Being able to pose a question for every presentaion. The core of the self-study activity will be preparation to the talk that is comparable to project implementation (a significant part of many regular courses). The students are expected to assign at the beginning of Term 2/6 and may drop the seminar till the beginning of Term 3/7 while credits are provided in Term 4/8. |
Elena Gryazina |
31 per term
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MA030489 | ||
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Research Seminar "Advanced Materials Science" (Term 1B-4)
This is a research seminar of the Skoltech Center for Energy Science and Technology and Materials Science Education program featuring presentations of young Skoltech researchers (MSc students, PhD students, postdocs) as well as external invited speakers. Every MSc and PhD student should deliver at least one presentation during the course. The range of topics is broad and includes any aspects of materials science. As a rule, each speaker presents results of his/her own research with a particular focus on research methodology. Every presentation is followed by a discussion.
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Sergey Luchkin |
30.75 per term
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DG030302i | ||
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Research Seminar "Modern Problems of Mathematical Physics" (Term 1-4)
Course "Modern problems of mathematical physics" is a student seminar, so participants are expected to give talks based on the modern research papers. Current topic of the seminar can vary from time to time. Topics that were already covered, or can be covered in the future, are: classical integrable equations, complex curves and theta-functions, quantum integrable models (quantum-mechanical and field-theoretical), models of statistical physics, stochastic integrability, quantum/classical duality, supersymmetric gauge theories, cluster algebras etc.
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Andrey Marshakov |
61.5 per term
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DG060268 | ||
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Review of Materials and Devices for Nano- and Optoelectronics 2
This is the second part of 'Review of materials and devices' started in Term 3. The lectures are presented by the scientists working actively in various directions of nanoelectronics and optoelectronics in Russia and abroad. Seminars assume the discussion of recent original papers in the area, as well as the classical papers presenting a physical basis for devices operation. Any student can take either part 1 (Term 3) or part 2 (Term 4) separately, with one presentation of paper. Alternatively, the student can take both parts and present two papers, one in Term 3 and jne in Term 4.
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Valery Ryazanov | 3 | MA030334 | ||
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Selected Topics in Data Science
In this course, we introduce the forefront of modern research in data science and familiarize Ph.D. students with state-of-the-art works in those areas. In particular, we introduce cornerstone subjects that are not commonly discussed in undergraduate or graduate Machine Learning classes. The course explores a wide range of topics including generative learning, self-supervised learning, metric learning, topological data analysis, neural architecture search, science-informed machine learning, reinforcement learning. The course aims to bring all students on the same page regarding the nature and orientation of state-of-the-art work in these field so that they acquire both depth and breadth of knowledge.
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Evgeny Burnaev | 6 | DA060492 | ||
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Smart Grids
Power systems around the world are undergoing a period of unprecedented change. A typical 20th Century power system was characterized by unidirectional flow of power from a limited number of large controllable power stations to a highly predictable demand. There was no energy storage so that at any time generation had to be equal to demand and the infrastructure utilization rates were low (about 55% for generation, 30% for transmission and even lower for distribution). Generally planning and controlling such a system was relatively straightforward as it was based around principles of deterministic hierarchical control, usually based on (N-1) reliability criterion.
On the other hand the emerging 21st Century power system is characterized by bidirectional flows between a very large number of uncontrollable and stochastic generators (usually, but not always, renewable ones such as wind or solar) and stochastic and often poorly-predictable demand. Demand ceases to be predictable as it consists of consumers equipped with smart meters and wind/solar generators hence possibly becoming net generators – so-called prosumers. Increased penetration of energy storage, both stationary and mobile due to a take-up of electric vehicles, offers buffering possibilities in dispatch (generation does not have to be equal to demand at any time). Controlling such a power system is the main research challenge in power systems and it is made possible by latest advances in ICT (Information and Control Technology), communication networks, Internet, GPS, sensors, etc. However it requires new tools and methodologies, the Smartgrid course will give the basis of this new grid scenario. |
Federico Martin Ibanez | 6 | MA060056 | ||
| Some Uses of Twistors in Field Theory (Term 3-4) | Alexey Rosly |
63 per term
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MA060601 | ||
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Startup Workshop
Startup Workshop (SUW) is the 6-credit E&I course designed to accelerate the Skoltech student/faculty/researcher teams developed and inspired by the Innovation Workshop and similar project-based E&I courses and programs (SFW, TEF, STRIP etc). Still, any Skoltech team is welcome to join the SUW through the mechanism of competitive selection. SUW course is extremely practical and pragmatic: its whole and only point is the preparation of the project application for the startup grant financing coming from two core Russian entrepreneurial infrastructural organizations: Skolkovo Foundation (SkF) and FASIE (aka Bortnik).
Despite such formal learning objective may look too narrow and mundane, it is proven to be beneficial to the project teams in two unique ways: — 1) building well-developed and structured SkF/FASIE application is an intensive exercise, that requires major effort in CustDev and prototyping, thus providing both learning and development to the initial project. Team that passed the SUW will be ready to face each and every venture investor of the world; — 2) as SUW teams enter the formal path of Skoltech Startup Funnel, they will obtain an intensive help from the Skoltech Dept of Business Devt, that will provide not only mentoring, but also some minor competitive financing, as well as E&I scholarship. SUW pushes teams through the preparation of the SkF/FASIE application that consists of 6 building blocks: 1) problem statement & validation, 2) solution & prototype description & validation, 3) competitive analysis and market assessment, 4) commercialization plan, 5) team and roles, 6) integrative 3-yr plan. Please note that SUW class is quite intensive: it starts well before the Term 2 with the competitive selection and requires serious work each week to produce the graded submission. |
Dmitry Kulish | 6 | E&I | MC060025 | |
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Superconducting Quantum Technologies
This course provides an overview of the rapidly developing field of physics of superconducting quantum systems. The course gives an introduction to basic phenomena of on-chip quantum optics in the microwave range, explain the quantum mechanical approach to superconducting circuits, study the interaction of electromagnetic waves with artificial atoms and provide examples of theoretical understanding and experimental realizations of these effects. Prior knowledge of quantum theory at the level of bra-ket notation and quantum evolution is assumed. Knowledge of superconductivity is not required.
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Oleg Astafiev | 6 | MA060340 | ||
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Technological Innovations: from Research Results to Commercial Product
The course is about transferring research results and discoveries into commercially successful products.
General approaches to commercialization of new technologies will be studied. However, the is really no standard path to commercializing scientific result; there are also no universal rules how distinguish a commercially perspective technology from many others. Intuition and skills come with practice. So, in this course, practical skills and experience will be developed. Students will go through multiple real cases of successful commercialization of research results in various technological fields – materials science, nanotechnology, photonics, space, chemistry, data science, energy etc. They will see and practice in how a research result from a laboratory can be transferred into a product, find its customer and market and, finally, become commercially successful. Opposite situations (that are, in fact, more common) will be studied equally carefully: when originally promising technologies did not make its way into a product – due to multiple reasons that will be analyzed. The lecturers will be balanced with practical work. The real-life conditions will be modelled as much as possible – aimed to develop hands-on skills and experience in critical evaluation of new technologies, comparing them to existing alternative solutions, finding a proper product realization and a market niche. Students are expected to go through technological innovation process themselves and then to compare, when possible, their results to the real stories of successes and failures. Students will also be greatly encouraged and motivated to apply what they learned to their current research – to consider their own results and/or results of their colleagues and collaborators from the point of view of commercialization potential, finding right target market, realizing a competitive product. |
Pavel Dorozhkin | 3 | E&I | MC030016 | CANCELLED |
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Technology Entrepreneurship Seminar: Advanced (Term 3-4)
Students having their own projects/ideas in development take this course to understand and practice advanced topics of technology entrepreneurship as well as to get the projects up to “external support/funding ready” level. This class typically follows the main E&I classes (e.g. as “Innovation Workshop” and “Technology Entrepreneurship Seminar: Foundation”, where students passed a basic opportunity recognition screening and customer discovery) and allows dedicated teams to continue development of their projects. Structured as practical and hands-on lab, the course typically has up to 6-7 projects under development. Students have the opportunity to present key “building blocks” of their ventures, receive advice from instructors and other members of the class, consult to other venture teams.
Admission to the course is by team and by project. We will select the participants from the teams took part in the course “Technology Entrepreneurship Seminar: Foundation” as well as newly applying teams. Upon the course completion, you and your team will |
Alexey Nikolaev |
31.5 per term
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E&I | MC030029b | |
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Thermodynamics and Transport at Nanoscale
The course introduces students to basic principles of thermodynamics and transport in “small systems, that is, the system whose size is comparable to the size of its major elements (be those molecules, micelles, polymer coils, etc). It covers small droplets, bubbles and crystals, stability of thin films, adsorption and deformation in nanoporous materials, stability of nanocolloids and nanocomposites, as well as transport and flows in micro-and nano- porous networks. In the process of studies, the students will also learn/reiterate the basics of interfacial thermodynamics, adsorption, wettability, spreading as well as the basics of transport of ionic and nonionic compounds in confined media. The lectures, seminars, homeworks and the final project will give practical example of calculations for all major classes of nanomaterials: nanoparticles and nanocomposites, microporous crystals, ordered and disordered mesoporous solids, and, finally nanostructured polymeric materials.
The course will be useful to all students willing to improve their understanding of natural (e.g. mineral oil) and man-made colloids, such as suspensions, emulsions and foams, as well as natural and synthetic porous materials. "Lectures" in the schedule refer to approximately 3h "windows" they will are not necessary scheduled as such, but will spread out and intermitted by seminars / recitations / HW and project discussions |
Vladimir Palyulin | 3 | MA030288 |
MOVEDmoved to Term 3
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Thermodynamics of Materials
The course provides a graduate level overview of selected topics of materials science related to formation of material and its stability. We will begin with the stability of materials by defining the energy contributions responsible for the stability including configuration, vibrational, and thermodynamic contributions to Gibbs free energy. Next, we will consider phase transitions and phase diagrams of materials with various dimensionality. One of the important factors responsible for stabilization is the formation of defects. Types of defects in bulk and 2D materials will be discussed. Considering all the above we will move to discussion of properties of surfaces and thin films which are the most important materials for sensing, energy storage, catalysis, and other applications.
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Aleksandr Kvashnin | 6 | MA060589 | ||
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Thesis Final Review
The Thesis Final Review is an assessment procedure that aims to determine whether a PhD student’s work is eligible for the PhD thesis defense.
The procedure also performs the function of the final state attestation. As a result of the Thesis Final Review the following decision can be made: Format: Scientific report in a format of PhD thesis draft and presentation. |
Nadezhda Dontsu |
6 per term
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DD480037 | ||
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 | ||
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Virtual Reality, Augmented Reality and Haptics
VR and Haptics technologies are booming in the world. Students of the course will learn how to make 3D environment and digital twins. They will create the VR copy of the robot, control its motion with tracking system, and literally touch the future technologies with the most recent electrotactile display provided by UEC (Japan). They will learn how to program ESP microcontrollers, digital filters, and to write the software for tracking the user fingers, arms with motion capturing systems. They will be taught the neuroscience of the human sense of touch, principles of haptics technologies and tactile displays for highly immersive VR, HRI, and HCI. The experimental design with ANOVA test will be presented. At the end of the Lecture, the students will have the chance to participate in the scientific content of the most prestigious conference in CG and novel technologies ACM Siggraph 2020 (Core A*). The invited speakers will be from such companies as Unity, NVIDIA, AntiLatency, Native Robotics (startup of ISR Lab.), Tsuru Robotics (startup of ISR Lab.), Sizolution (startup of ISR Lab.).
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Dzmitry Tsetserukou | 3 | MA030456 |
Summer
| Course Title | Lead Instructors | ECTS Credits | Stream | Course Code | Status |
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Academic Communication: Preparatory English for PhD Exam
As a PhD student, you should already know that effective professional communication is the key to academic success. Are you an ambitious person who wants to maximize their academic potential? Are you eager to boost your ability to write research papers, present in front of multidisciplinary audiences, participate in scholarly discussions and engage in other forms of academic communication — and do it all in good academic English?
Join this course and learn how to produce clear, correct, concise, and coherent texts related to your research, and how to present your data in front of a multidisciplinary professional community. You will be guided through all stages of paper writing, editing, peer-reviewing, and presenting. Academic communication is not limited to formal writing and professional presentation. As in a real conference environment, you will take part in networking activities, interacting with your peers from different fields, exchanging ideas and pitching your research achievements. The course is interactive, communicative and intensive, with various speaking, listening, reading and writing activities to be performed in class and at home, individually and in teams. By the end of the course, successful participants will |
Elizaveta Tikhomirova | 3 | Extra | DF030029 | |
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Industiral Immersion
The goal of Industrial Immersion is to provide for Skoltech students real hands-on work experience in industrial sector and develop the knowledge and skills for making impact through engineering and innovation. The Industrial immersion is performed in a company and it implies that internships at academic or research institutions (like universities etc.) are excluded. Duration of Industrial Immersion is 8 weeks. Project focuses on short-term development, manufacturing or operations challenges rather than long-term research problems and is co-supervised by the company and Skoltech. The internship is cooperatively planned: project assignment is provided by the company and subject for approval of the Industrial Immersion Program Coordinator (I.I.P.C.).
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Zhanna Turubarova |
12 per term
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Sector | MB120005 | |
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Pedagogical Experience
The main function of this course is to articulate Skoltech's expectations on PhD students who do their pedagogical TA assignment at Skoltech. The course describes the intended learning outcomes and how they are assessed.
The main bulk of the 81 hours of the course is spent in the actual courses in which the PhD-students do their TA-assignments. The assignments in the course itself include TA proposal and TA report and for cohorts 2022-2025 participation in training: Towards excellence in Teaching Assistantship”. Educational department should approve all assignments in Canvas in terms of content and formal requirements respectively. |
Dmitry Artamonov | 3 | DG030005 | ||
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Pedagogy of Higher Education
The course offers an introduction to facilitating learning in higher education for PhD students asked to act as teaching assistants. The course content focuses on high resolution constructive alignment of learning outcomes with learning activities and assessment strategies. Learning outcomes for a course are elaborated into separate activities and assignments for students. Learning outcomes need to be articulated at every level of learning activities from course to assignment.
The course also rests on the approach that learning is promoted by feedback. Participants in the course will therefore be required to plan and design effective use of continuous formative assessment. Such formative assessment requires strategic learning activities and assignments. The course therefore emphasizes communication-to-learn activities including peer learning. Skoltech is an English medium instruction environment, and the course explores ways of addressing the potential effects of language and culture barriers for high quality student learning. All topics in the course are applied by participants on their own teaching and learning experiences and are meant to be used as they prepare and plan for their teaching assistantships or their supervisory activities to come. All participants will have a task to produce a reflection on their future actions to evolve as facilitators and meet the requirements of the scholarship of teaching and learning. |
Aysylu Askarova, Daria Tokmeninova |
3 | DG030025 | ||
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Qualifying Exam: Computational and Data Science and Engineering
The Qualifying Exam is a compulsory 3 credits component of the doctoral program.
Its goal is to assess the PhD student knowledge and skills in the area of the thesis research. A Qualifying Exam includes the following components: The Doctoral Program Committee tailors the format and delivery mode of the Qualifying Exam to best suit the requirements of the doctoral program. |
Nikolay Brilliantov | 3 | DD030020cd | ||
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Research Immersion
Research Immersion will take place in Skoltech, as a part of Skoltech International Summer School on Mathematical Physics.
The program of the school includes modern topics of mathematical physics such as path integrals, topological effects, integrability, quantum groups, conformal field theory. Discussion of each topic will be dividedinto talks of participants. Preparation of these reports as well as discussion of them with experts is an essential part of the school's work. |
12 | Sector | MB120006m | ||
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Startup Founders Workshop
This is a “learning-by-doing” intensive course designed to provide science and engineering PhD students with a hands-on experience of translating your favorite technology into the innovative product and then the technology-based startup. The course will include lectures and individual mentoring sessions, as well as various project-based activities covering all stages of the new venture creation: from “getting out of the building” (literally!!! your team is to talk to customers, partners, users, etc.) and customers need identification, to defining the technological solution and the product, producing the prototype, and further to the business model designing and validating.
Admission to the course is by team and by project. The project themes may vastly range from digital to new materials and biomed. We suggest forming the projects around ideas inspired by your academic research, technologies you studied, prototypes you developed, etc. In all cases, you should choose something for which you have passion, enthusiasm, as well as technical expertise. The course is designed for wide diverse PhD student audience: The course flow is structured around 3 major stages (“Sprints”) comprising kick off intensive workshop, mentors and instructors supported projects development period, resulting capstone workshop and projects Demo Day. |
Alexey Nikolaev | 6 | E&I | DC060023 | |
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Thesis Proposal Defense
The Thesis Proposal Defense is a compulsory component of the program, whereby the PhD student defends a thesis proposal before the Individual Doctoral Committee.
The PhD student must develop in consultation with the supervisor, a thesis proposal in the form of presentation or written document. The proposal should contain the thesis research question, a proposal of an approach answering the question, a brief review of the literature, an overview of the proposed structure, the expected results, and a timeline to the thesis defense. The PhD student should provide the Committee members with a thesis proposal approved by the supervisor one week in advance of the defense, which resulted in the completion of the individual student digital assessment form by the Individual Doctoral Committee. |
Viktoria Mikhaylova |
6 per term
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DD060021 |