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Skoltech uses online platforms to support its teaching and learning activities. Information about courses is uploaded for the students on the special Student Information System, which is used for course registration.

Detailed information on each course (including the syllabus, announcements, home assignments, lecture notes and presentation slides) is posted on individual project sites dedicated to each particular course. As part of our collaboration with MIT, Skoltech uses online platform Stellar to support its educational activities.

Divided by academic terms below, you can find brief course descriptions of the courses offered at Skoltech during academic year 2014-2015

**FALL 2014 SEMESTER TERM 1**

**Term 1 Fall semester 2014**

**Course Description**

Silicon technology is a paradigm example of how solid physics discoveries can lead to a multi-billion industry employing thousands of engineers worldwide. As the scaling approaches its end, the revenues in high performance computing is declining. The business model of microelectronics industry is at the transition to mobile devices with new functionalities such as biological, chemical sensors integrated on a silicon chip, mobile devices etc.

The course will provide a graduate level overview of physical principles of logic devices, analog RF devices, and power electronics. The class will emphasize requirements for practical devices. In the ultimately scaled devices contact resistance determines the device performance. A special discussion will be devoted to resistance between 3D metal and 3D, 2D and 1D channel materials.

The course will rely on strong undergraduate math/physics background of the students, however no background in device physics will be assumed or required.

The lectures will start with introduction of basics of solid state physics, including Boltzmann Transport Equation, gradually moving on to increasingly more complex quantum mechanical description of transport.

In particular, the topics will include:

- bandstructure of solids determining saturated carrier velocity
- phonon scattering limiting intrinsic mobility
- Coulomb scattering limiting extrinsic mobility
- Landauer approach for contact resistance between interfaces
- materials beyond silicon, such as carbon nanotubes (CNTs) and III-V compounds actively explored by the microelectronics leaders IBM and Intel.
- novel 2D dichalcogenides materials and graphene will be reviewed

The course will employ case studies from industrial applications of advanced materials to nanotechnology. Several laboratories will give students direct experience with the basis for the simulation techniques of practical devices.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Explain the fundamentals of the modern silicon technology operations of devices.
- Understand industry requirements for novel devices aiming to replace silicon
- Apply fundamental knowledge about materials modeling to calculate electronic device characteristics
- Select a model appropriate for a given novel materials modeling study.
- Interpret and analyze the results of simulations
- Communicate, orally or through writing, the results of simulations to experimentalists and materials scientists.

**Number of ECTS credits: 6****
****Course instructors: ****Vasili Perebeinos **

**Term 1 Fall semester 2014**

**Course Description**

This course will provide a graduate level overview of modern energy systems, covering generation, conversion, transportation and end-use energy technologies. For each set of technologies we will first review the fundamental physics principles that are already extensively covered in most of the Russian undergraduate technical department curriculums. Next, we will assess the engineering challenges associated with the technologies, and discuss how to do basic cost-benefit analysis of possible approaches. Each section will be concluded by the analysis of modern trends in the areas and discussion of possible innovation and research opportunities. The pedagogy will include overview lectures and homework covering the more fundamental part of the material, as well as individual projects focusing on the analysis of novel technologies proposed in academic papers or introduced by various innovative companies. Guest lectures of industry representatives will present the major industrial company’s perspective on the key challenges and opportunities.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Explain global and regional energy flow diagrams, including characteristic scales, key technologies, and physical processes that are responsible for its structure.
- Explain the physics of main mechanisms of energy conversion and transport.
- Apply fundamental physics laws to describe the quantitative characterization of the main processes of energy conversion and transport.
- Describe the major technologies that form the foundation of modern energy systems.
- Discuss the key constraints and objectives that drive the innovation in the area.
- Describe recent innovations in the area.
- Critically assess innovation proposal feasibility using basic physics, technical, and economic analysis.

**Number of ECTS credits: 6****
****Course instructors: Petr Vorobev**

**Term 1 Fall semester 2014**

**Course Description**

This course aims to provide basic knowledge about manufacturing, properties, and contemporary problems in composite materials. The emphasis is on the practical applications, theoretical background, and the use of composite materials in industry. The course cuts across several domains, covering mechanics of materials, to manufacturing, to design and optimization:

- Introduction: What is a composite? Classification. Metals vs composites, advantages and disadvantages. Applications in industry.
- Manufacturing: Unidirectional vs. textile. Thermoplastic vs. thermoset. Prepreg vs. infusion, Additive manufacturing.
- Mechanics: Stresses and strains. Micromechanics. Ply. Laminate theories.
- Failure criteria.
- Defects. Fractal defect structures.
- Fatigue. Delaminations. Damage tolerance.
- Structural design and optimization.
- Finite element analysis. Abaqus.

Participants will learn basic fundamentals of these areas through active participation in teamwork. The course will provide practical knowledge on applications of composite materials in aerospace and mechanical engineering.

The lectures and seminars will include interactive software packages illustrating the mechanics of composite materials and allowing the students to formulate and solve their own projects. The course includes also practical experience of composite manufacturing and mechanical tests. During the last part of the course the participants will be presented a ‘challenge’ project in design and structural analysis, which they may attack analytically or by means of finite-element package Abaqus. Participants are expected to demonstrate their collective knowledge while at the same time solving individually a real problem.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Develop own point of view on broad variety of applications of composite materials.
- Discuss and make judgments about advantages and disadvantages of application of composite materials.
- Describe basic additive manufacturing concepts of composite parts and structures.
- Assess general challenges presented by the application of new materials in the industry.
- Conduct elementary engineering calculations to estimate the stiffness and strength of composite materials.
- Design and analyze individual projects using off-the-shelve software packages.
- Study how to use the Abaqus finite-element software suite.
- Practice communication techniques in written and presentation formats standard among professionals in the field of mechanical engineering.
- As an additional ‘challenge’ the students interested in mathematics will practice the fractals representing the fractal structure of defects.

**Number of ECTS credits: 6****
****Course instructors: Zafer Gürdal, Sergey Abaimov **

**Term 1 Fall semester 2014**

**Course Description**

The course will introduce the students to power system economics. After covering fundamentals of microeconomics , main types of electricity markets and regulation will be discussed including the Russian market. Economic dispatch and Optimal Power Flow with Locational Marginal Pricing will also be covered. The lectures will be supplemented by a laboratory exercise utilizing PowerWorld simulation package.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Understand why energy markets have been introduced in many countries
- Understand the principles of microeconomics and theory of the firm
- Know the main types of electricity markets
- Understand how the Russian power market operates
- Understand the principles of economic dispatch
- Understand how congestion management is handled using Optimal Power Flow and how Locational Marginal Prices are calculated
- Be able to use PowerWorld package to simulate power system engineering and economic operation

**Number of ECTS credits: 6****
****Course instructors: Janusz Bialek**

**FALL 2014 SEMESTER TERM 2**

**Term 2 Fall semester 2014**

**Course Description**

“Think Through Math” is an innovative mathematics course designated for graduate students specializing in the physical sciences and engineering. The goal of the course is to teach students to recognize, formulate and solve mathematical problems emerging from practical applications. The course will help students develop vital problem-solving skills preparing them for a career in industry or academia. The title “Think Through Math” reflects the aspiration that upon finishing this course a graduate student will be able to look at her or his research through the lenses of the appropriate mathematical ideas. To this end, the course incorporates ample amount of example applications ranging from chemical physics to computer vision.

The course will be covering such topics as variational methods, linear algebra, ordinary and partial differential equations, basic probability and statistics. To facilitate understanding of more advanced and unfamiliar concepts, the course is heavily relying on active learning techniques. The lectures are infused with hands-on activities and peer learning exercises. In addition to lectures, there are problem-solving workshops and discussion sessions. The total expected student workload is 20 hours peer week, split equally between classwork and individual home assignments.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Outline general ideas behind mathematical topics discussed in the course
- Recognize and formulate mathematical problems emerging from practical applications
- Select and apply appropriate tools to analyze such problems
- Formulate the expected form of the solution
- Solve an array of standard mathematical problems pertaining to the topics discussed in the course
- Visualize the obtained results

**Number of ECTS credits: 6****
****Course instructors: ****Anatoly Dymarsky **

**Term 2 Fall semester 2014 Study period: **October 27, 2014 – December 19, 2014

**Course Description**

This course provides students with the social science, management, and policy foundations necessary to understand why existing energy systems have developed as they have and what is required to change them, all in a global context. The global context will be provided by considering the geographic distribution of energy resources and the variation in patterns of energy capture, conversion and transmission, and end-use across Russia, the United States, and China.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Characterize the geographic distribution of energy resources and variation in energy development across Russia, China, US
- Analyze choices and constraints regarding sources and uses of energy
- Evaluate analysis and decision-making methods in complex energy contexts
- Explain the roles of markets and prices, government regulation, and other factors in energy systems
- Apply key analytical frameworks (e.g. economic, organizational, spatial, cultural) to describe and explain energy systems at various levels of aggregation (e.g. individuals, firms, governments)
- Integrate information into proposed policy alternatives

**Number of ECTS credits: 6****
****Course instructors: Amy Glasmeier **

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The goal of this course is to provide a survey of materials chemistry and surface spectroscopy techniques. Further emphasis will be placed on interfacial chemistry of materials surfaces and ex situ and in situ study using various surface sensitive spectroscopy methods.

The course will rely on strong undergraduate math/physics background of the students, however no background in materials will be assumed or required.

**Number of ECTS credits: 6****
****Course instructors: Keith Stevenson **

C**OMPLEX SYSTEMS - THE ORIGINS OF CATASTROPHES**

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

This course aims to provide basic knowledge about the catastrophic behavior of complex systems. The phenomena of phase transitions in thermodynamics have been well-known for hundreds of years. However, only recently, in the second half of the last century, it has been discovered that many non-thermal systems in biology, geology, engineering, economics, and social sciences exhibit similar catastrophic behavior. Examples include but not limited to the failure of a structure in engineering, the formation of petroleum clusters, the breakdown of dielectrics, a crisis in economics, traffic jams, etc. Each of these very different phenomena may be described as a catastrophe (or a phase transition) leading to the major change in the system’s behavior.

If all these phenomena are very similar to the well-studied case of phase transitions, the question appears: Would it be possible to apply the formalism of statistical physics to these phenomena? This would immediately provide us with a completely different point of view on the problem and would possibly allow us to prevent a catastrophe.

The course studies the applicability of statistical physics to complex phenomena. Three particular types of phenomena are chosen for the purpose of comparison: thermal fluctuations, percolation of petroleum oil, and damage leading to a catastrophe of a structure under loading. The course cuts across several domains of theoretical physics, covering:

- Fractals and multifractals.
- Statistical physics.
- Phase transitions.
- Damage phenomena.
- Fluctuations, correlations, fluctuation-dissipation theorem, susceptibility.
- Renormalization group.
- Scaling.
- Cross-over phenomena.
- Finite-size effect.
- The origins of catastrophes.

The course is considered to be mathematical and oriented on the formalism of theoretical physics. However, the course will not require prerequisite background in theoretical physics because all required concepts will be provided in the beginning of studies.

Participants will learn basic fundamentals of statistical physics and complex systems through active participation in teamwork. During the last part of the course the participants will be presented a ‘challenge’ project which they may attack analytically or numerically. Participants are expected to demonstrate their collective teamwork while at the same time solving an individual project.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Develop own point of view on broad variety of catastrophic phenomena.
- Judge the applicability of statistical physics to complex phenomena.
- Compare the applicability of the mean-field approach and the renormalization group transformation to a wide variety of phenomena.
- Explain scientific challenges currently present in the theory of complex systems.
- Analyze the origins of catastrophes, including damage of engineering structures.
- Design and analyze individual projects.
- Practice communication techniques in written and presentation formats standard among professionals in the field of statistical physics.

**Number of ECTS credits: 6****
****Course instructors: Sergey Abaimov**

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The course will introduce students to the basic notions of solid-state physics such as: periodic crystal lattices, phonons, Bloch theorem, properties of electronic bands in metals, semiconductors and insulators, conduction properties of various materials, the notion of Fermi-surface in metals, and magnetic phase transitions. Dependent on the level of the students, the course may also touch the topics of disordered solids, superconductors, and advanced experimental techniques. Upon availability, the course may include several laboratory experiments.

The course is intended for students who either never had a solid-state physics course or feel the need to strengthen the foundations of the subject. It is to be assumed that students previously had basic courses of quantum mechanics and statistical physics, but the relevant knowledge will be reintroduced whenever necessary.

The course will have a character of review. Lectures will cover the most important aspects of every topic, leaving a significant fraction of material for self-study and homework.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Become fluent in the basic concepts of solid-state physics
- Understand the approximate character of these concepts and recognize their applicability limits.
- Get practical experience to the framework of basic solid-state models.
- Improve their skills in applications of quantum mechanics and statistical physics

**Number of ECTS credits: 6****
****Course instructors: Boris Fine**

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The course includes lectures in petrophysics and reservoir fluids analysis, fundamentals of reservoir engineering including introduction to well testing, enhanced oil recovery and reservoir simulation

**The class will cover 14 main topics:**

- Introduction to Reservoir Engineering
- Petrophysical Evaluation of Reservoirs
- The General Material Balance Equation
- Hydrocarbon in-place, reserve and ultimate recovery estimations using volumetric method and material balance for single-phase gas reservoirs
- Hydrocarbon in-place, reserve and ultimate recovery estimations using material balance for under-saturated oil reservoirs
- Hydrocarbon in-place, reserve and ultimate recovery estimations using material balance for saturated oil reservoirs
- Single-phase fluid flow in reservoirs
- Productivity index
- Principal of superposition
- Introduction to pressure transient testing
- Introduction to reservoir simulation and history-matching
- Displacement of oil and gas
- Decline curve analysis
- Introduction to Unconventional Reservoirs

**Number of ECTS credits: 6****
****Course instructors: John Killough, Yucel Akkutlu, Berna Hascakir, Zoya Heidari, Eduardo Gildin **

**SPRING 2015 SEMESTER TERM 3 **

**Term 3 Spring semester 2015**

**Course Description**

The course will provide a graduate level overview of modern power systems, with a major emphasis on the system aspects of power production, transmission, storage and delivery. All stages of the power energy technical chain will be thoroughly reviewed from a comprehensive engineering prospective but also from the standpoint of fundamental physics principles/equations. A special emphasis will be given to improving students’ ability to extract well-formulated physics and mathematics problems from power engineering reality.

The course will rely on strong undergraduate math/physics background of the students, however no background in power systems will be assumed or required. In this course we will advance gradually through major principles of the power system design (with some history re-course), discussion of major elements of the power systems (generators, power lines, transformers), analyze state estimation, optimization, control and design practice, and will conclude with discussions of modern engineering, physics and mathematics challenges associated with the emergent smart/intelligent/resilient grid technologies.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Explain the general picture of the spatio-temporal scales and magnitude of energy production, transfer, storage and consumption in power systems at both transmission and distribution levels and related energy infrastructures.
- Explain basic operating principles and physics behind individual components and power system as the whole.
- Formulate optimization, control and planning problems in power systems.
- Select and apply the appropriate mathematical tools and algorithms to solve optimization, control and planning problems.
- Discuss major elements of the power systems (generators, power lines, transformers).
- Analyze state estimation, optimization, control and design practice.
- Discuss modern engineering, physics and mathematics challenges associated with the emergent smart/intelligent/ resilient grid technologies.

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course will provide a graduate level introduction to modern nanophysics.

Modern physics of condensed matter is now mostly the physics of nanostructures. These nanometer-size devices are nowadays used in virtually all modern applications like computing, information and communication technology, bio sensing, etc. Especially important are the optical applications, so called nanophotonics.

The course will rely on strong undergraduate math/physics background of the students in Quantum mechanics, Solid state physics and Classical electrodynamics.

The lectures will start with motivation for nanophysics, Moore’s law and Feynmann’s “Plenty of Room at the Bottom”. Then the idea of controlling the electronic properties by means of semiconductor heterostructures and quantum confinement will be introduced. Quantum nanostructures: classification, their general electronic and optical properties and manufacturing will be outlined.

In particular, the topics will include:

- electronic properties of semiconductor superlattices, quantum wells, wires, dots;
- transfer matrix and scattering matrix methods to calculate the electronic properties of nanostructures;
- electronic devices such as resonance tunnel diode, high electron mobility transistor;
- optical properties and excitonics of nanostructures;
- optical scattering matrix for nanostructured systems and nanophotonics;
- metallic nanostructures and nanoplasmonics;
- photonic structures: microcavities, photonic crystals, metamaterials; and
- principles of scanning tunneling microscopy and spectroscopy, single adsorbed molecule manipulation.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Explain the fundamentals and key concepts of the modern nanophysics and nanophotonics.
- Understand the principal electronic and optical properties of nanostructures and their main applications .
- Simulate quantum transport and optical (including excitonic) properties of nanostructures and photonic structures with transfer and scattering matrix.
- Understand the key concepts of scanning tunneling spectroscopy and manipulation of single adsorbed molecules.

**Number of ECTS credits: 6****
****Course instructors: Sergei Tikhodeev **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course covers the subject of carbon nanomaterials (fullerenes, nanodimond, 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 (by aerosol and CVD methods) and graphene, to observe the materials in transmission (TEM) and scanning (SEM) electron microscopes as well as by an scanning tunnelling (STM) and atomic force (AFM) microscopes and to study optical and electrical properties of the produced carbon nanomaterials.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- describe the electronic structure of various carbon nanomaterials;
- produce carbon nanotubes and graphene by substrate CVD method;
- interpret optical absorbance and Raman spectra of carbon nanomaterials;
- describe the mechanisms of the carbon nanotube formation;
- enhance presentation and scientific writing skills, group/team work skills.

**Number of ECTS credits: 6****
****Course instructors: Albert Nasibulin **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

This course aims to provide advanced knowledge about mechanics of composite materials, in particular with respect to their use in advanced structural applications. The emphasis is on theoretical background on fundamental characteristics, and practical and industry-relevant applications. The course cuts across several domains, covering primarily mechanics of materials and structures:

- Micromechanics: Models and homogenization
- Mechanics: Ply and laminate theories
- Multiscale modeling in CM
- Plate Higher Order Theories
- Design and analysis of composite structures for stiffened and sandwich components
- Effect of damage on composite structures. Designing with holes, delaminations, and impact damage
- Wave propagation in advanced composites for health monitoring

Each topic is presented by a Visiting Lecturer from Partner Universities (KU Leuven (Belgium), St. Etienne (France), TU Delft (The Netherlands), USC (USA)).

Participants will learn fundamentals of these areas through active participation in teamwork. The course will provide practical knowledge on applications of composite materials in aerospace and mechanical engineering.

The lectures and seminars will include interactive software packages illustrating the mechanics of composite materials and allowing the students to formulate and solve their own projects. The course includes also practical laboratory experience. During the last part of the course the participants will be presented a ‘challenge’ project. Participants are expected to demonstrate their collective knowledge while at the same time solving individually a real problem.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Become well versed in the field of structural analysis of laminated composites.
- Analytically and numerically solve various engineering problems related to mechanics of composite materials.
- Become aware of general challenges presented by the application of composite materials in the industry.
- Design and analyze individual projects using off-the-shelve software packages.
- Practice communication techniques in written and presentation formats standard among professionals in the field of mechanical engineering.

**Number of ECTS credits: 6****
****Course instructors: Zafer Gurdal, Sergey Abaimov**

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course will cover the following topics:

- Second quantization; path integrals in quantum mechanics
- Symmetries and broken symmetries. Conservation laws, hydrodynamics
- Symmetries and phase transitions. Goldstone theorem
- Topology of Bloch bands. Chern number and quantization of observables.
- Different types of symmetry breaking (SB). Spontaneous SB and phase transitions. Superfluidity and magnetism.
- Anomalous SB. Chiral anomaly and scaling anomaly in condensed matter systems.
- Free fermion systems. Interacting fermion systems. Landau Fermi liquid theory.
- Interacting one-dimensional systems. Luttinger liquids.
- Fractionalization of quantum numbers.
- Theory of quantum Hall effects. Aharonov-Bohm effect and fractional statistics.

**Intended Learning Outcomes**

The aim of the course is to twofold:

- Our first goal will be to discuss the concepts of condensed matter theory such as symmetry, topology and their relation to different types of ordering in many-particle systems.
- Our second goal will be to provide a gentle introduction to methods of quantized fields and their applications in many-body physics. We shall try to emphasize the physical and visualizable aspects of the subject. While the course is intended for students with a wide range of interests, many examples will be drawn from condensed matter physics and atomic physics.

**Number of ECTS credits: 6****
****Course instructors: Leonid Levitov **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

Mathematical modeling is an essential tool of modern engineering. The purpose of the course is to provide the basic tools of mathematical modeling for understanding, predicting and controlling properties of realistic technologically important materials and processes as well as to develop practical skills to use them. An essential part of this course is the team work on educational projects. This work includes: formulation of the problem, identification of the most important processes underlying the problem under consideration, formulation of the problem in terms of mathematical models, analysis of the models by means of analytical tools and computer simulation, and comparison with experimental data. These projects will include examples of mathematical modeling from Electrical Engineering, Mechanics and Hydrodynamics, Energy, Space Engineering, Photonics and etc.. MATLAB will be used as the main software tool. Intermediate results on the projects will be delivered at the Midterm presentation and overall results at the Final presentation.

List of topics (modeling tools) include:

- Ordinary differential equations (closed form solutions, power series, approximate methods, qualitative analysis, numerical solutions);
- Integral transforms (Fourier and Laplace transforms);
- Partial differential equations (separation of variables, Green’s function, integral transform);
- Eigenvalues and eigenfunctions;
- Perturbation theory;
- Calculus of variations;
- Nonlinear partial differential equations.

**Intended Learning Outcomes**

Upon completion of this course the students will be able to:

- Use basic methods of mathematical modeling including computer modeling
- Formulate the problem in terms of mathematical model
- Compare and select mathematical methods for addressing the problem under consideration
- Analyze mathematical model
- Interpret obtained results in terms of original problem
- Apply fundamental knowledge concerning modeling methods via computer simulations including terminology, key concepts, methods and topics of study
- Compare results with available experimental data
- Make a scientific/engineering presentation
- Communicate results in writing (report on educational project).

**Number of ECTS credits: 6****
****Course instructors: Ildar Gabitov **

**Term 3 Spring semester 2015****Study period: **February 16, 2015 – March 27, 2015** Course Classification: **Sector

**Course Description**

The goal of the course is to provide an overview of nuclear power technology and economics:

- Introduction to nuclear physics and neutronics (2 lectures)
- Nuclear power reactors
- Nuclear safety and radiation protection
- Fuel cycle. Reprocessing and closing the fuel cycle. Final disposal.
- Nuclear power economics
- Generation IV Sodium Fast reactors (invited lecture)
- Non-power applications and co-generation with NPPs (invited lecture)

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- How nuclear reactor works, what are advantages and challenges of different types of reactors
- What are different components of the nuclear fuel cycle (uranium mining, enrichment, spent nuclear fuel storage and reprocessing, final disposal of the spent nuclear fuel)
- Health effects of radiation, historical nuclear accidents (TMI, Chernobyl, Fukushima).
- Fundamentals of nuclear power economics
- Future reactors: Challenges.

**Number of ECTS credits: 3****
****Course instructors: Alexey Lokhov, Petr Vorobev **

**SPRING 2015 SEMESTER TERM 4 **

**Term 4 Spring semester 2015**

**Course Description**

Thermal-Fluid Sciences course is designed for Skoltech students who need exposure to key concepts in the thermal-fluid sciences in order to successfully apply them in research programs of various Skoltech CREIs. The course includes Thermodynamics, Fluid Mechanics, and Heat and Mass Transfer.

**Topics for Thermodynamics Part:**

- Properties of a Pure Substance
- First Law of Thermodynamics and Energy Equation
- Energy Analysis for a Control Volume
- The Second Law of Thermodynamics
- Entropy
- Second-Law Analysis for a Control Volume

**Topics for Fluid Mechanics Part:**

- Fluid Statics
- Elementary Fluid Dynamics—The Bernoulli Equation
- Fluid Kinematics
- Finite Control Volume Analysis
- Differential Analysis of Fluid Flow
- Similitude, Dimensional Analysis, and Modeling
- Viscous Flow in Pipes
- Flow over Immersed Bodies
- Compressible Flow

**Topics for Heat and Mass Transfer Part:**

- One-Dimensional, Steady-State Conduction
- Two-Dimensional, Steady-State Conduction
- Transient Conduction
- Introduction to Convection
- Forced Convection: External Flow
- Forced Convection: Internal Flow
- Free Convection
- Heat Exchangers
- Diffusion Mass Transfer

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Understand the closed system and open system/control volume concepts and be able to describe engineering problems in terms of these concepts.
- Understand the concepts of an equation of state and be able to use such an equation to describe pure substances. This includes understanding and being able to apply various property tables.
- Understand the first and second laws of thermodynamics and learn how to apply these laws to both open and closed systems.
- Understand how materials store energy and the relationship between the energy storage and phase changes in materials.
- Be prepared for the thermodynamic applications discussed in fluid mechanics section of the course.
- Be prepared to use fluid mechanics terminology.
- Understand and be able to use fluid statics concepts and equations.
- Understand the proper applications of Bernoulli’s equation.
- Use thermodynamic control volume concepts in fluid kinematics for applications that include mass balances, momentum balances, energy balances, and the Reynolds transport theorem.
- Understand the uses of fluid differential equation representations of statics and dynamics.
- Have a basic understanding of internal and external viscous flows and examples of applications.
- Be able to understand incompressible, subsonic, and supersonic relationships including isentropic flow relationships and normal shock relationships.
- Identify and understand the various mechanisms of heat transfer that characterize a given physical system.
- Be able to formulate models for heat conduction processes. Apply analytical and numerical methods to solve one- and two-dimensional conduction problems.
- Be able to combine thermodynamics and fluid mechanics principles to analyze heat convection processes.
- Be able to integrate radiation aspects into real-world global heat transfer problems.
- Use computer technology, methods and languages to write programs to solve complex heat transfer models.
- Analyze and design complex heat transfer applications, such as heat exchangers.
- Be able to apply the engineering design procedure to a problem.
- With the help of design project, develop skills that would apply to lifelong learning.

**Number of ECTS credits: 6****
****Course instructors: Iskander Akhatov**** **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course will provide a graduate level overview of modern concepts of light-matter interaction and the variety of applications of the photonic resonances in modern science and technology. The class will emphasize the use of different theoretical approaches in description of the photonic resonances and will give as well the understanding of the major role of the concept of the resonance in physics.

The course will rely on strong undergraduate math/electrodynamics background of the students, however some basics concepts will be remind to facilitate the understanding of the program.

- In particular, the topics will include:
- Introduction (to be made coherent with other lectures).
- Concept of resonance. The oscillator, mechanical model, external force, displacement, phase, frequency dependence near the resonance.
- Nonlinear oscillator. Classical pendulum. Dependence of the period on the amplitude, multiple harmonics.
- Coupled oscillators. Frequency splitting. Lattice of the oscillators.
- Classical electrodynamics (Electrostatics, effective parameters, maxwells eqs)
- Metallic particle in electric field (plasma frequency, Drude’s formula, skin layer, plasmons)
- Separation of scales in nanoparticles
- Examples of electromagnetic resonances: Plasmon of sphere and dimmers. Electromagnetic fields distributions near the resonances. Stacked structures. Plasmonic resonance (electrostatic approximation)
- Metallic sphere
- Metallic dimers
- Metamaterials (Examples; applications, provide explanation that in this case effective parameters approach is not relevant, nonlocal magnetism, nanolasers-spasers, review of experimental results)
- Basics of quantum plasmonics.
- Hyperbolic metamaterials
- MC, PCS, WGM structures general similarities, some experimental figures.
- Theory of the resonances. Scattering matrix concept. Poles of the scattering matrix, resonant vectors. Simple examples.
- Interaction with nanostructures (1): light emission of isolated nano-object embedded in resonant structure, nano-antennas.
- Controlling of light emission by design of nano-antennas.
- Interaction with nanostructures (2): interaction of the resonances with an ensemble of nanoobjects, polariton formation, weak and strong coupling. Exciton polaritons, plasmons.
- Introduction to nonlinear effects, reference to other lectures.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Understand the basic properties of resonances
- Main principles of modern photonics
- Compare available methods and computational tools in terms of their similarities and differences as well as their strengths and limitations.
- Apply fundamental knowledge about electrodynamics and solid state physics to the problems of photonics.

**Number of ECTS credits: 6****
****Course instructors: ****Nikolay Gippius**, **Ildar Gabitov **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course will review recent developments in the physics of graphene, a two-dimensional crystal of carbon atoms, which is believed to have a huge potential in electronics. The course will provide a comprehensive self-contained theory of electron transport in graphene, crucial for understanding and predicting the properties of graphene-based nanomaterials.

The course requires basic undergraduate math/physics background and introductory knowledge of quantum mechanics. Necessary elements of advanced quantum mechanics will be provided to the students throughout the course.

The lectures will start with the overview of the basic properties of graphene, gradually moving on to disorder, coherence and interaction effects and, finally, to the physics of topological quantum matter.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Understand the key concepts in physics of graphene and related materials.
- Apply contemporary methods for the analysis of quantum transport in nanostructures.
- Predict electronic properties of novel two-dimensional materials.
- Understand results published in research papers and communicate them to the audience.

**Number of ECTS credits: 6****
****Course instructors: Mikhail Skvortsov **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course will build on the Introduction to Power Systems course, developing at graduate level a wide range of topics in recent smart grid development as detailed below. The goal is to give students familiarity with such topics and competence in handling them, in the context of present and future distribution and transmission networks.

**Topics:**

- Introduction to Smart Grids, drivers, challenges and regulatory issues
- Renewable energy and distributed generation and its effect on the grid
- Low carbon loads and their effects on the grid
- Smart metering
- Demand side response
- Electrical energy storage
- Active network management and automation
- Advanced power flow management, including real time thermal rating, generator integration and control, back-to-back convertors, use of storage
- Advanced voltage control in networks
- Power flow laboratory
- Decentralised control and microgrids
- Offshore and island networks
- Introduction to application project based on stand-alone microgrid

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Describe and evaluate the purposes, costs and benefits of a wide range of smart interventions on the network
- Compare different interventions in the context of particular future challenges on particular regions of network, and make justified recommendations as to which should be considered for adoption.
- Critically compare such interventions with more traditional solutions such as network reinforcement.

**Number of ECTS credits: 6****
****Course instructors: ****Phil Taylor, Haris Patsios, Neal Wade, Simon Blake, Padraig Lyons**** **

**FALL 2014 SEMESTER TERM 1**

MACHINE LEARNING

**Term 1 Fall semester 2014**

**Course Description**

The course introduces major machine learning algorithms theoretically and gives practical skills to apply these algorithms to real data. Strengths and weaknesses of the algorithms are analyzed in both simple model contexts and real-life contexts.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Formulate in mathematical form major machine learning tasks:

– classification

– regression

– clustering

– visualization

- Prepare data for analysis:

– dimensionality reduction

– feature extraction

– manage missing data

– outlier filtering

- Identify/Recognize major machine-learning algorithms:

– Bayesian classifier

– K-Nearest neighbors

– SVM

– decision rules/trees

– artificial neural nets

– compositions of algorithms

- Assess quality of algorithms
- Apply machine learning algorithms using open-source toolboxes and libraries.

**Number of ECTS credits: 6****
****Course instructors: Konstantin Vorontsov, Viktor Kitov **

OPTIMIZATION METHODS

**Term 1 Fall semester 2014**

**Course Description**

This course is an application-oriented introduction to optimization. It will focus on modeling real-world engineering tasks as optimization problems and using state-of-the-art optimization techniques to solve the resulting models. The course will provide appropriate theoretical background and will go through a range of standard optimization models (e.g. Linear programs), as well as advanced optimization techniques required to handle more challenging optimization tasks. Application domains will include (among others) information engineering/machine learning, power engineering, transportation and logistics, structural design.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Formulate/model real-world engineering tasks as optimization problems, including a catalogue of standard optimization programs (e.g. Least-squares, Linear Programs, Quadratic Programs, Unconstrained optimization)
- Use optimization packages; interface with them using MATLAB, Python, or other high-level programming languages
- Understand the theoretical notions and underpinnings behind the optimization techniques, such as convexity, Lagrange duality, optimality conditions, etc.
- Understand the issues in applying standard optimization packages and approaches, understanding their limitations, understand the effect of equivalent reformulations on the performance of standard methods
- Handle large-scale and/or hard optimization problems through the use of advanced optimization techniques (such as equivalent reformulations, constraint/column generation, dualization, branch-and-cut)

**Number of ECTS credits: 6****
****Course instructors: Victor Lempitsky **

NUMERICAL LINEAR ALGEBRA

**Term 1 Fall semester 2014**

**Course Description**

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, where they are applied to the solution of linear systems, eigenvalue problems and several others. 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 several other IT Skoltech courses, including Fast PDE, Optimization and Great Computational methods. It will also serve as a first-time where programming environment and infrastructure is introduced in a consistent manner.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Solve medium-scale numerical linear algebra problems (solve linear systems, compute eigenvalues and eigenvectors) using matrix factorizations
- Solve large scale numerical linear algebra problems using iterative methods
- Find which linear algebra tools are appropriate for the particular applications
- Select and use an appropriate software library for the abovementioned tasks

**Number of ECTS credits: 6****
****Course instructors: Ivan Oseledets **

**FALL 2014 SEMESTER TERM 2**

GREAT COMPUTATIONAL METHODS

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

This course is an introduction to computational techniques for modeling and simulation of a large variety (e.g. aerospace, mechanical, electrical, energy and biomedical) of engineering and physical complex systems

Topics include techniques for automatic assembly of mathematical problems from physics’ principles; sparse, direct and iterative solution techniques for steady state analysis of linear complex systems; Newton methods for nonlinear systems; Time domain and periodic steady state simulation; techniques for model order reduction of complex dynamical systems.

The focus is on a learn-by-doing philosophy.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Recognize and formulate mathematical structures (e.g. conservation laws and constitutive equations) common to a lot of complex engineering and physical systems, intended as networks of interconnect dynamical components (e.g. mechanical and structural frames, oil/blood/heat transport networks, integrated circuits and nation-wide electrical energy transport networks, bio-chemical reaction networks and many others)
- Select, implement and if needed modify the appropriate type of formulation (e.g. nodal analysis, node-branch) for the description of complex systems.
- Select (and modify) or implement an appropriate steady state solver (e.g. sparse LU vs. iterative methods) for a given linear or linearized complex system description
- Select, implement and modify an appropriate strategy (e.g. damping, source/load stepping, homotopy, etc.) to facilitate initialization and convergence of a Newton solver for a given nonlinear complex system
- Select and implement an appropriate integrator (e.g. implicit vs. explicit, lower order vs. high order, stable vs. A-stable) for the time domain simulation of a given complex system

**Number of ECTS credits: 6****
****Course instructors: Thanos Polimerides, Jacob White, Luca Daniel **

BAYESIAN METHODS - ADVANCED MACHINE LEARNING

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The course addresses Bayesian methods for solving various machine learning and data processing problems (classification, dimension reduction, topic modeling, collaborative filtering, etc). Bayesian approach to probability theory allows one to take into account user’s preferences and task specific properties when building the model. Besides, it offers an efficient framework for model selection. We will cover the problems of automatic feature selection, determination the number of components in probability mixtures, estimation the dimension of latent subspace, setting the regularization coefficients in an efficient way, etc. We will review several simple models that can be used as building blocks for the construction of more complex probabilistic models. General tools for building the probabilistic models and for designing inference algorithms in those models are presented in the course. We will end up with the basics of the probabilistic graphical models which are further extension of Bayesian framework.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Apply existing advanced Bayesian models of data processing; know their pro et contras
- Build their own probabilistic models for the particular problem
- Develop either exact or approximate inference algorithms for given probabilistic models
- Formulate the domain-specific knowledge in terms of prior distributions
- Read and discuss research papers on probabilistic framework in machine learning, computer vision, collaborative filtering, text processing, etc.

**Number of ECTS credits: 6****
****Course instructors: Dmitry P. Vetrov **

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

Advanced data management technologies are employed when one navigates an online map, books a ticket, or searches for people in an online social network. The objective of this course is to study advanced modern topics in data management. Topics will include multidimensional index structures, query optimization, similarity search, time series data, spatial data, data mining, graph data, adaptive indexing, database security, and modern data storage and query processing systems. Students will have the opportunity to practice research skills, such as reading and evaluating original research papers and communicating complex technical material, and participate in an innovative team research project of their choice.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Identify data management problems and assess the issues of efficiency, scalability, and effectiveness inherent in them.
- Propose and implement solutions to data management problems using modern techniques.
- Review the related literature in a critical manner and identify strengths and weaknesses.
- Form effective teams—work in a team, coordinate activities, and deliver a result of teamwork.

**Number of ECTS credits: 6****
****Course instructors: Panagiotis Karras **

**SPRING 2015 SEMESTER TERM 3 **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course introduces you to basics knowledge of methods for robot design, simulation, and control of robotic system. Topics include robot kinematics, dynamics, control, design, simulation, motiond planning, and AI. The course slightly touches the research in wearable, space and telexistence robotics. The projects in robotics done by the lecturer, such as NurseSim, iFeel_IM!, FlexTorque, NAVIgoid, TeleTA will be presented. The lecture aims at student preparation and motivation to conduct projects in Robotics, Automation, Advanced manufacturing, and Intelligent Systems.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Demonstrate the knowledge in hardware and IT technologies for Industrial and Service Robotics
- Design the 3D CAD model of robots
- Produce the components and structures with 3D printer
- Implement the robot control algorithm
- Program the microcontrollers (Arduino, myRIO)
- Evaluate the performance of robotic system
- Develop skills in robot programming and simulation.

**Number of ECTS credits: 6****
****Course instructors: Dzmitry Tsetserukou **** **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course will give students a practical sense of current capabilities of Language Technologies, their underlying principles and business applications. It will cover 6 major components of Language Technologies: Information Retrieval, Information Extraction, Machine Translation, Speech Recognition, Text and Speech Generation, and Communication with Devices. Each area will be covered with an overview lecture, practical exercises and a discussion of trends and applications. Last 2 weeks will be dedicated to short projects by groups of students. Utilization of the state-of-the-art Open Source and commercial tools will be emphasized.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Articulate the key principles and methods underlying Language Technologies
- Assess applicability of Language Technologies for solving business problems
- Select appropriate tools
- Build simple applications

**Number of ECTS credits: 6****
****Course instructors: Anatole Gershman **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

Most engineers must learn the tricks of the trade by asking help from more experienced friends and through a laborious trial and error process. However, there are few reference books addressing how to program multiprocessors. This course aims to change this state of affairs, providing a comprehensive presentation of the principles and techniques available for programming multicore/multiprocessor machines.

The course will begin by covering the theoretical foundations of programming on multicore machines (we are strong believers that good practice requires understanding the theory). It will then move on to cover the real-world techniques used to program them. It will include a sequence of programming assignments of increasing difficulty, culminating with the design of a highly parallel “firewall” application, running on a state-of-the-art 80-way multicore machine.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Analyze both the foundations and the practice of multiprocessor and multicore programming.
- Build intuition and confidence in reasoning about concurrency.
- Describe the fundamentals of the underlying architecture to reason effectively about the performance of a concurrent data structure.
- Interpret the fundamentals of concurrent data structure design.
- Design and implement his/her own concurrent data structures.

**Number of ECTS credits: 6****
****Course instructors: Nir Shavit, Alexander Matveev, Sadegh Nobari, Panagiotis Karras **

**SPRING 2015 SEMESTER TERM 4 **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

Representation learning (and deep learning as its most important particular case) is arguably the hottest topic in Machine Learning. Over the last few years, deep learning has led to several breakthroughs across a variety of application domains, including speech recognition, computer vision, and, more recently, natural language processing, and bioinformatics. The course will cover the basics and the recent advances on RDL. The course will be practical in nature, with intense work on Python/MATLAB assignments, and application projects on large-scale machine learning.

While there will be a certain bias towards computer vision/image data, other application domains will be also covered in details.

**Topics:**

- Basic RL techniques: k-means, PCA, sparse coding
- Large-scale retrieval systems, quantization and product quantization, inverted indexing
- Low-dimensional embeddings, metric learning
- Large-scale discriminative learning, stochastic gradient descent
- Deep networks, backpropagation
- Autoencoders, deep stochastic neighborhood embedding (SNE), Siamese architectures
- Convolutional neural networks (CNN)
- Applications of CNNs in computer vision
- Recurrent neural networks (RNN), long short-term memory (LSTM) networks and speech recognition
- Applications in natural language processing, word2vec
- Applications of RLDL in molecular biology/genomics

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Understand main concepts and algorithms underlying representation learning and deep learning
- Implement deep learning/representation learning systems and apply them to new data/new domains
- Train RLDL architecture on large datasets with the help of Graphical Processing Units (GPUs)
- Identify machine learning architectures suitable for a certain tasks
- Analyze the performance of learned architectures

**Number of ECTS credits: 6****
****Course instructors: Victor Lempitsky**** **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course covers numerical methods for partial differential equations (PDEs). The focus on (a subset of) prototypical examples of elliptic, parabolic, and hyperbolic PDEs, and on the methods of finite differences and finite elements. The students will be exposed to notions of approximation, stability, and accuracy.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- For various types of PDEs, propose suitable numerical methods and implement them.
- Assess stability and accuracy of numerical methods

**Number of ECTS credits: 6****
****Course instructors: Alexander Shapeev, Ivan Oseledets **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The objective of this course is to introduce principles and methods of data mining and knowledge discovery, and let students be involved in meaningful real-life data mining projects to cope with challenging problems. Topics of interest will included, but not be limited to, pattern discovery, similarity search, data clustering, classification, association rules, and analysis of different data types, such as, sets, graphs, and sequences.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Identify data mining problems and assess the potential to gain knowledge from data using data mining techniques.
- Propose and implement solutions employing modern data mining principles.
- Study and critically assess literature related to data mining problems.
- Work in a team, collaborate in a productive manner, coordinate activities, and deliver the teamwork result.

**Number of ECTS credits: 6****
****Course instructors: Panagiotis Karras, Sadegh Nobari **

**SPRING 2015 SEMESTER TERM 4 **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

This course teaches students how to design and control precision machines. The goal is for students to be able to follow a deterministic process to design a machine, to model the machine mathematically, and to design and implement a controller for the machine based upon the system model. Topics include the deterministic design process, fundamental electrical and mechanical design principles, component selection, system modeling, and controller design. Solidworks, MATLAB, and LabVIEW will be used throughout the class. Students will design, build, and test a complete motion control system for an application of their choosing.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Model and simulate physical systems
- Measure and control a physical system
- Brainstorm and select a concept by following a structured design process.
- Select and integrate the components for a precision motion control system
- Apply fundamental mechanical design principles to design a robust precision machine
- Apply design for manufacturing techniques to a precision machine
- Control a physical system with a lead/lag controller
- Create a part using a 3D printer.

**Number of ECTS credits: 6****
****Course instructors: ****Matthew Gilbertson, Brian Anthony, Eric Gilbertson**** **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **Sector

**Course Description**

This course introduces students to the business side of the Product Design and Manufacturing sector and provides a comprehensive overview of various types of products/manufacturing like space, aeronautics, automotive, electronics, heavy equipment and consumer products. The course includes a seminar series from manufacturing sector executives and other key stakeholders from industry and research organizations. Topics covered include: overview of design and manufacturing, product development overview in a few economic sectors, global perspective of manufacturing systems, business overview of a few manufacturing sub-sectors like automotive, aerospace, micro-electronics and heavy equipment and others.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Describe the main sub-sectors or services within the Product design and manufacturing sector;
- Analyze the main product types, technologies and systems employed and assess the technological barriers;
- Analyze the parameters of the business structure of a number of the sub- sectors through annual reports of a few key companies;
- Integrate into an analysis that indicates potential avenues of development in the Design and manufacturing sector.

**FALL 2014 SEMESTER TERM 1**

(SPACE) SYSTEMS ENGINEERING

**Term 1 Fall semester 2014**

**Course Description**

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 lifecycle management of a system, encompassing conception, design, implementation, AIT, operations, and disposal of systems, with special emphasis on space systems applications. Being a foundational course for the Space students of Skoltech, the course discusses in detail *space* systems engineering, introducing students to the main architectural elements of a space mission (launch segment, ground segment, space segment) and to general ideas of spacecraft design which will be discussed in more detailed in the Satellite Engineering course offered at Skoltech. The course also discusses systems architecture principles, which are introduced in more depth in the follow up Systems Architecture course at Skoltech.

The Systems Engineering course follows the systems engineering Vee-model as an educational guideline throughout the term. Tradeoff analysis and systems architecture will be introduced as part of the course, but more detailed coverage of these topics will be provided by the ad-hoc Systems Architecture course already in place at Skoltech. The course includes a journal club to review academic articles and standards pertinent to systems engineering, which form a complement to weekly homework assignments and a design project that is conducted throughout the term.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Design and tailor a lifecycle of a system, with particular emphasis on space projects.
- Identify the main architectural elements of the space mission and its main stakeholders.
- Design a systems engineering management plan for a complex system.
- Conduct tradeoff analysis and simple systems architecture studies during the early stages of a design project.
- Review the state of the art of systems engineering research from relevant literature sources and identify new areas of investigation in the field.
- Construct and conduct teamwork towards the development of complex engineering systems.

**Number of ECTS credits: 6****
****Course instructors: Alessandro Golkar **

**FALL 2014 SEMESTER TERM 2**

SPACE SECTOR

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

This course examines the domain of space from multiple vantage points—space as a business, a way of life, a fulfillment of human dreams. In addition, it examines space-related issues that drive key international regulatory, economic, and global policy. To gain insight into these different dimensions, we examine space through three different lenses: sub-sectors, technologies, and organizations.

Part 1: Sub Sectors:

- Launch services and markets
- Satellite manufacturing and operations, including sensors and payloads
- Space data products
- Space communications and navigation services
- Space science payloads and missions
- Military space
- Human spaceflight, programs and policies

For each one, we would discuss the organization of the sub-sector, what value it produces, how it is funded, the commercial and governmental organizations that participate, how it is regulated, and technology barriers

Part 2: Technologies:

- Technology readiness, and sources of technology, technology planning
- Launch technologies and options
- Satellite technologies
- Payload technologies (may need to do in parts)

Part 3: Organizations

- Space sector organizations (verticals and horizontals)
- “New Space”
- Space agencies and other international agents (NASA, ESA, etc.)

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Describe the main sub-sectors or services within the space business
- Describe the main technologies used and the technological barriers
- Describe the business models of each of the sectors, and where value is generated and how, including traditional and “new” space
- Describe the funding of R and D, regulation and government programs in space

**Number of ECTS credits: 6****
****Course instructors: ****Edward F. Crawley **

SPACE ENVIRONMENT

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

This course is designed for scientists and aerospace engineers. The course will introduce students to a variety of scientific and engineering problems related to space environment. Overview of the history of space exploration. Introduction to basic plasma physical processes occurring on the Sun, in the solar wind, and on terrestrial and planetary magnetospheres and ionospheres. Kinematics of charged particles, and wave-particle interactions. Radiation environment of the Earth and outer planets. MHD. Solar-planetary coupling processes, aurora. Orbital decay, biomedical consequences of space radiation, spacecraft charging, and single event upsets. Space physics exploration missions and mission design. Course project will be focused on the analysis of observations from Van Allen Probes, THEMIS, Polar, NOAA, ACE and other missions. Project will require computer skill to read data stored in various formats. Student will learn how to process large amounts of space data, analyze data using data mining and visualization tools.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Identify the effects of space environment on the technology in space and on the ground.
- Distinguish different regions of space environment.
- Contrast different phases of the solar cycle and identify the variation of different with radiation with the solar cycle activity.
- Describe how radiation affects astronauts and high altitude flyers
- Distinguish radiation effects associate with single even upsets, sensor degradations and launch delays.
- Distinguish between the inner and outer magnetosphere. Describe the shape of the mangetophere. Appreciate the similarities between the mangetosphere and heliosphere.
- Identify how does society depend on technology in space and why space weather is a fast growing field.
- Distinguish between solar storms, and substorms of various scale and identify potential space weather effects.
- Describe the motion of charged particles in the electric and magnetic fields.
- Compare and contract different regions of the Heliosphere.
- Make estimates of the main time and length scales in space plasmas.
- Interpret ACE solar wind observations.
- Describe source of ionization
- Describe and calculate the force on a charged particles and calculated it’s trajectory
- Distinguish between electrostatic and electromagnetic plasma waves and also between parallel perpendicular and oblique plasma waves.
- Determine the pitch-angle and how it changes along the bounce orbit for the radiation belt paticles.

**Number of ECTS credits: 6****
****Course instructors: Yuri Shprits **

**FALL 2014 SEMESTER TERM 1**

STRUCTURE AND FUNCTION OF NUCLEIC ACIDS

**Term 1 Fall semester 2014**

**Course Description**

The course emphasizes quantitative aspects of the DNA and RNA science. It covers all aspects of the field: mathematical, chemical, physical, biological and engineering. Biophysical models used in the field are covered. Special at attention is given to biotechnology application of the DNA and RNA science.

The goal of the course is to give the students with physical, mathematical and engineering background the in-depth knowledge and understanding of structure of DNA and RNA, DNA and RNA biophysics and biological functions of DNA and RNA and their biotechnology applications.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Name main chemical and physical features of nucleic acids and their structures including: duplexes, triplexes and quadruplexes
- Describe fundamental role of DNA and RNA in the cell, including main cellular processes: replication, transcription and translation.
- Apply their knowledge to main biotechnology tools of DNA and RNA, such as Polymerase Chain Reaction (PCR), Real Time PCR, DNA chips, anti-gene and anti-sense gene silencing, RNAi technology, in vivo gene edit-ing, etc.
- Calculate DNA and RNA biophysical characteristics, such as equilibrium melting temperature of the double helix, the kinetics of strand separation for specific DNA sequences, DNA bending and torsional flexibility, the formation of unusual structures (Z-form, cruciform, H-form) in super-coiled DNA, etc.
- Design PCR primers and probes, design probes capable of forming triplexes with target sequences, etc.
- Evaluate the most appropriate physical method(s), out of many (X-ray crystallography, Nuclear Magnetic Reso-nance (NMR), electron microscopy, cryo-electron micros-copy, gel electrophoresis, optical methods, fluorescence, etc.), to solve the problem of interest.

**Number of ECTS credits: 3****
****Course instructors: Maxim Frank-Kamenetskii **

GENETIC ANIMAL MODELS AND INTEGRATIVE PHYSIOLOGY IN DRUG DISCOVERY

**Term 1 Fall semester 2014**

**Course Description**

The course emphasizes genetic in vivo models as essential element of drug target discovery, verification and mechanistic evaluation. It covers all aspects of the field: principles of genetic modifications, various vectors for transgenes, specific recombination in ES cells (reporter knock ins, knockouts, conditionsl knockouts) , genome editing (TALEN, CRISPR). Special at attention is given to RNAi translational medicine. It also gives basic methods in integrative physiology and contemporary imaging methods on molecular, cellular, organ and whole animal levels. Cardiovascular, renal, pulmonary, immunological, neural and cancer models will be discussed with illustration of major in vivo protocols.

The goal of the course is to give the students with biological, chemical, physical, mathematical and engineering background the in-depth knowledge and understanding unraveling causative factors of disease and targeted intervention using genetic intervention and integrative physiology approach .

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Genetic engineering, gene targeting, gene editing in ES cells and in the embryo.
- Transgenesis using different experimental platforms.
- RNAi technology in gene silencing
- Analysis of physiological phenotypes in vivo.
- Evaluation of complex physiological experiments on transgenic, knock down models with good understanding of biomedical physiology of the mouse.

This course will be sufficient minimum to join the lab at a PhD student/Junior postdoc level working in biomedicine/translational medicine

**Number of ECTS credits: 3****
****Course instructors: ****Victor Kotelianski, Yuri Kotelevtsev **

DRUG DISCOVERY

**Term 1 Fall semester 2014**

**Course Description**

The course is aimed at students who are new to the field of drug discovery. We outline the basic concepts and processes of drug discovery. The goal of this course is guidance through different stages of drug development process from bench findings to a clinical study. Assay development, High Throughput Screening, data analysis, and lead optimization.

We will also introduce common methods and modern technics. A high emphasis will be placed on critical discussions of to-date methodology.

Animal in vivo models as essential element of drug target discovery, verification and mechanistic evaluation. It covers all aspects of the field: principles of genetic modifications.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Design target identification study;
- Design cell based assay for High Throughput Screening and implement appropriate technic;
- Data analysis and strategy for cherrypicks
- Biochemical assay and compound mechanism of action
- Critical review results and give short oral presentations on “generated data”
- Design the study for structural activity relationship.
- Animal dieses models
- General toxicology

**Number of ECTS credits: 3****
****Course instructors: Alex Yuzhakov, Raul Gainetdinov **

BASIC MOLECULAR BIOLOGY TECHNIQUES

**Term 1 Fall semester 2014**

**Course Description**

The course provides hand-on experience with the general techniques used in the molecular biology/biotechnology lab.

The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Apply their knowledge to plan and perform simple experiments in the molecular biology lab;
- Understand applicability and limits of use of molecular biology techniques;
- Use basic bacteriology methods; perform PCR, cloning, mutagenesis, expression and purification of recombinant proteins, knock-outs of E. coli genes; read and analyze routine DNA sequences and peptide fingerprints for protein identification with mass-spectroscopy.

**Number of ECTS credits: 3****
****Course instructors: Konstantin Severinov **

**FALL 2014 SEMESTER TERM 2**

CLINICAL TRIALS AS AN ESSENTIAL PART OF THE INNOVATION PROCESS IN THE PHARMACEUTICAL DEVELOPMENT

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The course aims to give students a general understanding of the clinical trials (CT) process in drug development from preclinical requirements and outcome up to post-marketing research as well as approaches to discover new therapies. The class will cover history, qualification of clinical trials, international and local regulations, Good Clinical Practice as well as practical aspects related to organization and logistics of different projects, use of drug discovery toolkit. The topics will include:

- Clinical Trials History
- Clinical trials qualification with in depth introduction to every type of CT
- Good Clinical Practice (ICH-GCP)
- Government regulation of clinical trials in the US, EU, and Russia
- CT organization at the study level: Biopharmaceutical companies’ research & development (R&D) departments and Contract Research Organizations (CROs)
- CT organization at the research center level: Academic Centers, Site Management Organizations (SMO), private practices. Countries differences
- Clinical Trials in Russian Federation: local specifics
- Preclinical and Phase I clinical research
- Identification and evaluation of diseases with unmet medical need
- Understanding of how to define / study disease mechanisms
- Disease modeling in vitro and in vivo
- Therapeutic platforms: choosing the right modality (cell therapy, gene therapy, small molecules, polymers, biologics)
- Translational biomarkers based on biofluids

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Explain the difference between and objectives of preclinical and Phase I, II, III, IV clinical trials
- Describe the main international principals of the modern clinical trials and name geographical regulatory differences
- Assess the compliance of a clinical study with ICH-GCP
- List the key regulations on CTs in Russia
- Analyze the influence of local regulations on drug development in a certain region
- Choose the appropriate path for innovative drug development
- Compare CT organization at a R&D department at Pharma / Biotech and a CRO and discuss different outsourcing strategies
- Define specifics of SMOs
- Calculate enrolment rate and prepare recruitment schedule based on preliminary feasibility analysis (exercises)
- Estimate a length of a clinical study based on a study synopsis and feasibility results (exercises)
- Compare Russian and European requirements for preclinical research
- Recognize the highest risks in First-in-Man trials and explain the importance of regulations and accurate study design in the area
- Identify diseases with unmet medical need
- Name the tools to define / study disease mechanisms
- Describe disease modeling process
- Explain the approaches to choose the right modality
- Discuss biomarkers’ role in drug development
- Debate the role of a state in drug innovation
- Review CTs market in a certain country (homework)
- Prepare a report on CTs in a certain therapeutic area using available Internet resources: www.clinicaltrials.gov and others to demonstrate the key trends and specifics (project)
- Create a presentation on CT regulations in a certain country (homework reported at lessons)

**Number of ECTS credits: 3****
****Course instructors: Eugene Selivra **

MATHEMATICAL MODELING IN POPULATION AND SYSTEMS BIOLOGY

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The course aims to teach students to quantify biological observations into conceptual models, frame these models in mathematical terms, and analyze these models, both qualitatively and computationally. It includes considering:

- Strategies to choose the relevant variables, parameters and observables, model nature (discrete vs continuous), modeling technique (agent-based simulations vs. dynamical system approach), and result interpretation.
- Reaction-diffusion systems and pattern formation
- Aggregation and fragmentation
- Evolution and ecological dynamics, Logistic and Predator-prey models
- Networks

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Define the scope and scale of the model, its accuracy and predictive power (proof of the principle vs. numerically accurate)
- Identify the variables, parameters and mechanisms, relevant for the considered phenomenology and ignore the unnecessary ones
- Formulate the corresponding equations
- Implement the numerical simulations, visualize and interpret the results

**Number of ECTS credits: 3****
****Course instructors: Jaroslav Ispolatov**** **

MOLECULAR BIOLOGY

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

This is an advanced course of molecular biology. Structure of the course is based on the flow of information in gene expression. The first part of the course is devoted to DNA biosynthesis, including replication, recombination and repair. Additionally, introduction into representative phage and viral replication will be provided. Second part of the course goes about RNA biosynthesis and includes transcription and processing. The third part of the course is about protein biosynthesis.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Explain the mechanisms of DNA, RNA and protein biosynthesis
- Explain the basic principles of the regulation of replication
- Explain the basic principles of transcriptional control of gene expression
- Explain the principles of translational control
- Design an experimental plan to study a gene, a process or a control mechanism

BIOINFORMATICS, COMPARATIVE GENOMICS AND SYSTEMS BIOLOGY

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The course will cover modern bioinformatics tools, methods and approaches. It will cover:

- Basic bioinformatics tools
- Comparative-genomics methods of functional annotation, metabolic and regulatory reconstruction
- Methods of analysis of NGS-based data (genome sequencing and resequencing, transcriptomics, protein-DNA interactions; epigenetics; 3D chromatin structure)
- Methods and tools of structural bioinformatics.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- identify and solve bioinformatics problems in experimental context;
- plan and perform data analysis in large-scale experiments; meaningfully combine various types of data;
- predict protein structure, dynamics, and interactions.

**Number of ECTS credits: 3****
****Course instructors: Mikhail Gelfand **

STEM CELLS

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The Stem Cells course is a lecture course that includes the complete spectrum of biological and medical perspectives from fundamental basic biology of stem cells and mechanisms of regeneration through evaluation of pluripotent stem cells for therapeutic purposes.

The course offers a broad overview in research strategies and state-of-the-art cellular, molecular and genetic approaches for advancing stem cell research.

The course consists of lectures given by leading experts in a field, discussions and informal seminars.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Explain the main characteristics of stem cells
- Describe the differences between embryonic and adult stem cells
- Describe reprogramming strategies
- Name and describe different adult stem cells
- Explain genomic control in stem cells
- Explain regenerative processes in different model organisms
- Explain current therapeutic usage of stem cells
- Discuss future perspective to use stem cells in regenerative medicine, cancers and stem cell gene therapy

**Number of ECTS credits: 3****
****Course instructors: Anton Berns, Marianna Bevova **

BASIC MOLECULAR BIOLOGY TECHNIQUES

**Term 2 Fall semester 2014
Study period: **October 27, 2014 – December 19, 2014

**Course Description**

The course provides hand-on experience with the general techniques used in the molecular biology/biotechnology lab.

The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Apply their knowledge to plan and perform simple experiments in the molecular biology lab;
- Understand applicability and limits of use of molecular biology techniques;
- Use basic bacteriology methods; perform PCR, cloning, mutagenesis, expression and purification of recombinant proteins, knock-outs of E. coli genes; read and analyze routine DNA sequences and peptide fingerprints for protein identification with mass-spectroscopy.

**Number of ECTS credits: 3****
****Course instructors: Konstantin Severinov **

**SPRING 2015 SEMESTER TERM 3 **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

the field of neuroscience. We outline the basic concepts and processes of brain function ranging from molecular to cognitive neuroscience. The course aims to teach students to understand the structure and function of neuronal communication in physiology and pathology at the molecular, cellular and system levels. They will learn about brain-related diseases and pharmacology of central nervous system disorders such as ADHD, addiction, schizophrenia, bipolar disorder and Parkinson’s disease. We will also introduce common methods to study brain function. Particularly, we will introduce electrophysiological, optogenetic, imaging, voltammetric and microdialysis techniques for the study of brain function. A high emphasis will be placed on critical discussions of most up-to-date methodology.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Discuss how electrical signals are generated in neurons;
- Discuss major concepts in cognitive neuroscience
- Describe how defects in the neurotransmitter function will lead to inherited diseases;
- Discuss major experimental methods used to study brain function in vitro and in vivo;
- Critically review and give short oral presentations of original research papers;
- Design an experimental plan to study the structure and function of brain disorders and to search for new therapeutic options
- Design the study for analyzing neurotransmitter or receptor function

**Number of ECTS credits: 3****
****Course instructors: Raul R. Gainetdinov **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course focuses on molecular mechanisms of development and differentiation in invertebrate and vertebrate model systems, including embryonic stem cells. Attention will be given to major signaling pathways, gene regulatory networks and quantitative models.

The goal of the course is to provide the students an overview of principles of development across model systems. This basic knowledge is essential for regenerative biology and biotechnology.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Identify common principles of developmental mechanics in model organisms, including formation of germ layers and body axes.
- Name major signaling pathways and provide examples of their role in tissue and organ specification across model organisms.
- Explain principles and functioning of spatio-temporal developmental gene networks; their connection with gradients of developmental determinants.
- Understand quantitative models for development, develop ability to formulate simplest dynamic models in the form of ordinary differential equations.
- Develop broad vision of the field, required for innovative experimental design and biotechnology (construction of transgenic animals, generation of pluripotent cells) as well as theoretical modeling of developmental gene networks.

**Number of ECTS credits: 3****
****Course instructors: Dmitri Papatsenko **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course is aimed to students in deep literature research using modern and classical publications at the Molecular Biology field.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Experiment design
- Data generation, analysis, and research strategy in the presented material
- Proper selection of the positive and negative controls for each experiment
- Critical results review and give short oral presentations

**Number of ECTS credits: 2****Course instructors: ****Alexander Yuzhakov**

** **

**SPRING 2015 SEMESTER TERM 4 **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **Science, Technology, and Engineering

**Course Description**

The course provides experience with the scientific project planning and implementation in the molecular biology/biotechnology lab.

The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.

**Intended Learning Outcomes**

Upon completion of this course the students will be able to apply their knowledge in molecular techniques to plan and implement simple projects in the wet lab.

**Number of ECTS credits: 3****
****Course instructors: Konstantin Severinov **

**SUMMER TERM 2014**

INNOVATION WORKSHOP

**Course Description**

The purpose of this intensive workshop is threefold: to create a foundational experience in E&I for all, to empower participants to identify and solve real-world problems with technology, and to instill an entrepreneurial “can-do” attitude in the culture of this first cohort. Participants engage in experiential learning to prototype whole technology innovations. That is, they iterate all the components of an innovation: the problem to solve, the technology to solve it, the possibilities for impact, and the vehicle to bring the proposed innovation to life.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Engage in the joint optimization of the system-technology-problem-impact
- Demonstrate the ability to advocate for a technology innovation by translating uncertainty into actionable steps and milestones
- Appreciate that innovation is easier done than reasoned in abstract
- Reason about technology innovation as a trans-discipline endeavor (beyond technology development, tech transfer and tech management)
- Prototype an innovation leveraging engineering principles and systems thinking
- Appreciate the structure required to bring an innovation to impact, embracing business and entrepreneurial principles as an integral part of the technology innovation process
- Discuss the different forms of impact and the many aspects they share (learn to identify gradients where others see gaps)
- Participate in imagining new visions for products, research, and/or organizations, including identifying issues, thinking creatively, defining problems and road-mapping solutions
- Develop an appreciation for technology development, its flexibility, and accessibility by engaging in quick technology development projects
- Appreciate structural, organizational and outreach principles by which innovations evolve to attain impact
- Leverage interpersonal dynamics and leadership.

**Number of ECTS credits: 6 Course instructors: Ilia Dubinsky **

**FALL 2014 SEMESTER TERM 1**

IDEAS TO IMPACT: FOUNDATIONS FOR COMMERCIALIZING TECHNOLOGICAL ADVANCES

**Term 1 Fall semester 2014**

**Course Description**

Technological Innovation is critical to the survival and competitiveness of emerging and existing organizations. This course lays the foundation to undertake a robust analysis and design of opportunities for technology-based commercialization. We introduce tools and frameworks that help isolate and control the factors shaping the identification, evaluation and development of commercial opportunities. Throughout the course we use technology examples originating from problem sets found in engineering and scientific education to develop the skills necessary to connect technology and impact.

The course is designed to help students develop the ability to find, evaluate, and develop technological ideas into commercially viable product and process concepts, and build those concepts into viable business propositions. The material covered is research and theory-based but the course is practice-oriented with much of the term spent on shaping technology-based opportunities. A central objective of this subject is to equip students with an understanding of the main issues involved in the commercialization of technological advances at both strategic and operational levels.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Apply innovation theories and concepts to the rigorous identification and development of new opportunities for societal and commercial impact.
- Reconcile tools and methods learned in the context of an engineering education with the need to assess and design an opportunity that will bring them to use.
- Forge technology-based ideas into workable business concepts and learn how to test them in the marketplace.
- Differentiate and distinguish the different process activities associated with new product/process/service development, inside or outside an established firm.
- Explain the concepts of customer development and business model development.
- Critically assess and evaluate the resource assembly junctures in the development of new ventures (whether they be within established corporations or start-ups).

**Number of ECTS credits: 6****Course instructors: Max von Zedtwitz **

**SPRING 2015 SEMESTER TERM 3 **

**Term 3 Spring semester 2015****Study period: **February 2, 2015 – March 27, 2015** Course Classification: **E&I

**Course Description**

Intellectual property (IP) is a critically important aspect of technological innovation and a key factor in the management of technology-intensive enterprises. Prowess in the management of intellectual property is important for technology leaders in both established corporations and entrepreneurial ventures.

Entrepreneurial technology ventures flourish according to how well their intellectual property assets are managed, leveraged and enforced. Additionally, it is almost impossible for engineers or scientists to avoid confronting issues related to intellectual property. These include: the risk of violating the IP rights of others; an obligation to respect the IP policies of one’s employer; the need to obtain IP protection for one’s own inventions and creative works; the obligation to become involved in the management of the IP belonging to ones employer; and the challenge of ensuring that one’s own IP rights are not infringed by others, including by one’s own employer or one’s clients. In addition, with such a large amount of contemporary business—in both the private sector and government—involving outsourcing and inter-organizational collaboration, expertise in the licensing of intellectual property rights is in high demand.

This course will survey basic concepts of intellectual property and provide an introduction to a variety of types of intellectual property and IP-related rights, such as patents, copyright, trade secrets, trade marks, design rights, database rights, domain names, and demarcations of origin. The will also examine the strategic management of IP in the process of technology commercialization, and the resolution of IP-related conflicts between technology based enterprises. It will also explore social, economic and ethical issues associated with the accumulation and exploitation of intellectual property.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Identify, differentiate and understand the various of types of intellectual property.
- Articulate and explain a variety of ways in which intellectual property plays a role in technology commercialization.
- Intelligently discuss the integration of intellectual property with the innovation strategies of technology organizations.
- Identify intellectual property risks associated with technology commercialization.
- Understand the fundamentals of accumulating, managing, implementing and enforcing IP rights, as well as appropriating value from IP assets.
- Know the fundamentals of designing and negotiating IP licenses.
- Appreciate approaches to resolving IP-related conflicts between organizations.
- Think critically about the international dimension of IP management.
- Identify and analyze ethical and social issues associated with intellectual property.

**Number of ECTS credits: 6****Course instructors: Kelvin Willoughby **

**SPRING 2015 SEMESTER TERM 4 **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **E&I

**Course Description**

Technological innovation requires not only the creation of new inventions—inspired by science, engineering or practice—but also the creation of appropriate organizational vehicles to facilitate real technological impact in society and the environment. After a plausible technical idea has been developed and a potential business concept has been identified, successful commercialization of the technology typically requires the creation of either a new venture or a new unit within an existing organization. This course will explore the creation, development and management of new technology ventures, including private start-up companies and other types of business organizations, paying special attention to the role of scientific or technical entrepreneurs as founders or leaders of such ventures. The advantages and risks, as well as challenges and opportunities, for individuals choosing a career pathway in entrepreneurship will also be examined.

This course will focus attention on two especially important dimensions of managing entrepreneurial technology ventures: the constant challenge of assembling the resources—financial, human, material and organizational resources, among others—that are required to operate the business; and the art of iteratively and concurrently managing the processes of technology design, product and/or service design, and market analysis. It will adopt an international perspective as well as a domestic national perspective on entrepreneurship.

The course will have as its backbone a group project in which student teams will conduct a strategic analysis of the entrepreneurial prospects of a novel technology idea, as well as develop a proposed strategy for a new venture to implement that idea. Student teams may work on either a technical idea of their own (perhaps developed as a prototype in another Skoltech course or project) or on a technical idea generated by others from Skoltech, subject to approval by the instructor.

The course will also include a series of lectures, lively classroom analyses of entrepreneurial technology cases, guest presentations by technology entrepreneurs, various classroom learning exercises, and lively classroom discussion of contemporary topics in technology entrepreneurship.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Understand the importance of entrepreneurship as a pathway to facilitate technological innovation.
- Appreciate the difference between technology entrepreneurship and other forms of business organization.
- Develop a strategy for commercializing a prototype of a new technology through the organizational vehicle of a new technology venture.
- Comprehend and apply the full range of elements, factors and schema that are appropriate to be incorporated in to a new technology venture plan.
- Work as part of a team to develop multiple iterations of a new technology venture plan to be used in communication with potential stake-holders in a new venture.
- Appreciate the critical role of the entrepreneur as a leader in new technology ventures.
- Understand the process of gaining access to resources to support a new technology venture.
- Make informed and rational decisions about the critical step of proceeding to launch a new venture.

**Number of ECTS credits: 6****Course instructors: Kelvin Willoughby, Zeljko Tekic **

**Term 4 Spring semester 2015****Study period: **March 30, 2015 – May 29, 2015** Course Classification: **E&I

**Course Description**

The course teaches introductory knowledge, skills and provides useful framework for how entrepreneurs and founders of startups raise institutional money to fund building of their companies.

**Intended Learning Outcomes**

Upon completion of this course, the student will be able to:

- Describe the process of raising money and necessary elements of preparation
- Describe the potential sources of funding and how they differ
- Explain the concept of “A Round Crunch” and its meaning for their startups
- Specify the market norms and standards in structuring venture capital deals
- Discuss the critical elements of negotiating strategies and tactics
- Obtain the optimal valuation for their ventures
- Summarize the critical elements in the relationship between venture capitalists and entrepreneurs and analyze the optimal way to build these relationships
- Identify the priorities in the funding processes.

**Number of ECTS credits: 6****Course instructors: Ilia Dubinsky **

Click to see course catalogue AY 2013-2014