Computational Science and Engineering

General description
A successful graduate of this educational track will
Know

  • Mathematical and algorithmic foundations of Computational Science & Engineering as well as main aspects of High Performance Computing (HPC).
  • Statements of all major computational problems in science and engineering as well as main approaches/techniques to solve them.
  • State of the art techniques of scientific computing, mathematical modelling and visualization.
  • Main methodological aspects of both, scientific research and application development in Computational Science and Engineering.
  • How to apply different scientific computing techniques and algorithms (including HPC) to real-world problems in natural&social sciences and various industrial sectors.

Be able to

  • Identify, formalize, and solve outstanding computational problems in real-world applications.
  • Understand and formulate new models of complex systems arising in natural&social sciences and engineering.
  • Choose the most appropriate method to solve a particular computational problem.
  • Apply relevant software tools, computer languages, data models, and computational environments for modelling and visualization.
  • Contribute in the development of the next-generation scientific computing software competitive with or superior than the existing examples of software in critical and emerging application fields (such as oil&gas, material design, big data, aerospace, and pharma).
  • Integrate different components of computational tools and HPC hardware to produce computational solutions for real-world tasks.
  • Work with technical literature (e.g. perform an effective bibliographical research, read and critically analyze scientific articles, use scientific metrics and most important databases).
  • Communicate results of analysis effectively (visually and verbally) to different audiences (specialists, users, stakeholders etc).

Other core aspects of the program are industry immersion and the entrepreneurship and innovation component. All courses are taught in English. Skoltech offers international environment and the opportunities for students to visit foreign universities and international conferences.

Core courses

  • Scientific Computing
  • Numerical Linear Algebra
  • Computational Science and Engineering I and II

Click to see Skoltech Course Catalogue

Entrepreneurship and Innovation component
It includes two courses, one of which is the Innovation Workshop – a trademark Skoltech course introducing newly enrolled students to the culture of entrepreneurship and technological innovation.

Industry internship
8-week full-time internship in one of Russian or foreign companies.

Elective courses and individual research projects
You choose elective courses that fit best your career plans.

Master thesis
Master thesis is the central part of the program. Skoltech Faculty & Research staff members offering Master projects in Data Science are: Mikhail Belyaev, Evgeny Burnaev, Mikhail Chertkov, Maxim Fedorov, Grigory Kabatianski, Stamatios Lefkimmatis, Viktor Lempitsky, Ivan Oseledets, Maxim Panov, Athanasios Polymeridis, Alexander Shapeev, Vladimir Spokoiny, Dzmitry Tsetserukou, Dmitry Yarotsky, Alexey Zaytsev and Andrii Zhugayevich.

They are affiliated with the Center for Computational Data-Intensive Science and Engineering as well as with other CREIs.
The program is accredited in the framework of the educational standard of Russian Federation 02.04.01 Mathematics and Computer Sciences.