Master of Science in Mathematics and Computer Science
Modern science and engineering critically rely on efficient and fast computational techniques and models. ACS program achieves the synergy of state-of-the-art mathematical modeling methods (numerical ODE and PDE, stochastic modeling, machine learning and Big data-based approaches) and their implementation with modern high performance parallel computational facilities furnished with up-to-date software. The cutting-edge scientific MSc project solidifies the theoretical knowledge obtained in the courses.

Skolkovo Institute of Science and Technology
Skoltech is a new global research university created in collaboration with MIT. The new campus, designed by Herzog de Meuron, is home to 40 world-class labs with best-in-class equipment, globally renowned professors and students from 40+ countries.
Mode, duration:
2 years (full-time)
no tuition fees for those who successfully pass the selection
Student pack:
monthly stipend (40 000 RUB), medical insurance
the application period for 2021 start is now closed
The MSc program is 2 years long: the first year is to strengthen your theoretical background, and the second year is to focus on research. Students have the freedom to choose courses and extracurricular activities to shape their individual trajectory, acquire soft skills, and gain entrepreneurial skills to prepare for employment.
Lectures and practical classes conducted by world-renowned professors and experts.
Students' individual research projects carried out at Skoltech laboratories.
An 8-week summer industry immersion program at leading companies turning knowledge and skills into action.
Courses on entrepreneurship and innovation that provide skills, as well as knowledge, to commercialize ideas and research findings.
+ Academic mobility at leading global universities (on a competitive basis)
A successful graduate of the program will be capable of:
handling the available information about real-world tasks and shaping it into a form of efficiently solvable mathematical models
developing new computational approaches and algorithms for data-intensive problems
using High-Performance Computing techniques in Python and C/C++ to develop and/or optimize massively parallel computer codes
utilizing modern frameworks for data visualization
Data-Intensive Mathematical Modelling and Simulations (DIMMS)
This track aims at fostering a new generation of computational scientists and engineers, able to combine first principle and data-driven approaches in mathematical modeling of natural, industrial and social phenomena. The curriculum carefully balances advanced computing, machine learning, and computational physics to implement large scale models in modern computational environments.

A successful graduate of this track will be able to:
  • construct mathematical models of industrial processes, natural, and social phenomena based on fundamental principles and available data
  • contribute to the development of efficient algorithms and codes for computationally demanding, data-intensive modeling and simulations
  • apply relevant computational approaches, data structures, hardware, and software to complex real-world problems.
High Performance Computing (HPC) and Big Data
The modern computational world is essentially parallel as CPUs and GPUs contain multiple cores. Datasets and computational problems are becoming impossible to be processed using a single compute node.

Besides pursuing an academic career, HPC track students with knowledge of modern computing architectures, programming, code optimization, and distributed deep learning will easily find Data Scientist, Software Engineer, or IT-specialist positions in various industries, including IT, Oil & Gas, Finance & Banking, Industrial R&D, Manufacturing and more.

A successful graduate of this track will be able to:
  • Effectively address modern computing world challenges using existing and state-of-the-art HPC and Big Data frameworks in a variety of applications (deep learning, data analytics, mathematical modeling of complex events)
  • Solve mathematical modeling and data-intensive tasks using parallel computing
  • Develop and optimize massively parallel computer codes
  • Create efficient infrastructures for HPC clusters, Big Data, and Data Centers
Program structure:
The 2-year program comprises of compulsory and recommended elective courses on the most important topics, a wide set of elective courses (depending on the research and professional needs of the student), components of entrepreneurship and innovation, research activity and 8 weeks of industry immersion.

Track "Data-Intensive Mathematical Modelling and Simulations"
Track "High Performance Computing (HPC) and Big Data"

Compulsory and Recommended Elective courses (36 credits)

Compulsory Сourses:
Scientific Computing
Numerical Linear Algebra
Machine Learning

Recommended Electives:
Introduction to Data Science
Numerical Modeling
High-Performance Computing and Modern Architectures
Foundations of Software Engineering
High-Performance Python Lab
Efficient Algorithms and Data Structures
Introduction to Linux and Supercomputers
Advanced Solvers for Numerical PDEs
Neuromorphic Computing
Parallel Computing in Mathematical Modeling and Data-Intensive Applications
Soft Condensed Matter
Stochastic Methods in Mathematical Modeling
Foundations of Multiscale Modeling: Kinetics
Introduction to Digital Pharma
Omics Technologies
Thermodynamics and Transport at Nanoscale
Machine Learning in Structural Bioinformatics and Chemoinformatics
Biomedical Mass Spectrometry
Multi-omics Technologies for Precision Medicine

Elective courses (24 credits)

Entrepreneurship and Innovation (12 credits)
Technology Entrepreneurship: Foundation
Entrepreneurial Strategy
Leadership for Innovators
Hack Lab: Laboratory for Ideas
Startup Workshop
Ideas to Impact: Foundations for Commercializing Technological Advances
Biomedical Innovation and Entrepreneurship
IoT: Launching New Products & Startups
Business Communication
Technology Planning and Roadmapping: Foundation
Technology Planning and Roadmapping: Advanced
Intellectual Property, Technological Innovation and Entrepreneurship
Technology Entrepreneurship: Advanced
Technological Innovations: from Research Results to a Commercial Product
Developing Products and Services through Design Thinking

Research and MSс thesis project (36 credits)

Industrial Immersion (12 credits)
Academic Partners:
  • Moscow Institute of Physics and Technology, Russia
  • Keldysh Institute of Applied Mathematics, Russia
  • National Research Center "Kurchatov Institute", Russia
  • Tomsk State University of Control Systems and Radioelectronics (TUSUR), Russia
  • Higher School of Economics, Russia
  • ETH Zürich, Switzerland
  • Nokia Bell Labs, UK
  • MIT, USA
    Industrial Partners:
    • Severstal
    • GazpromNeft
    • NVidia
    • Bruker
    • BioCAD
    • Chemrar
    • Insilico Medicine
    • КРОК
    • Niagara
    Main research areas:
    • Mathematical and Supercomputer Modelling
    • Big Data and distributed deep learning
    • Modern Computing architectures and technologies
    • Efficient Numerical Algorithms
    • Soft Matter and stochastic processes
    • Physics for machine learning and machine learning for physics
    • Physics for social sciences
    • Mathematical modeling of large-scale complex phenomena (plasmas, multi-component, and multi-phase fluids and gases)
    • Drug design and computational design of new pharmaceuticals
    • Reinforcement learning for target search, flock formations
    • Distributed graph analytics on modern supercomputing architectures
    • Modeling of geomechanics for the oil industry
    • Femtosecond optics
    • Large-scale molecular modeling and optimization of properties of new chemicals
      Research groups:

      Our graduates shape their own futures by choosing from a variety of career opportunities
      in industry, science and business:
      Landing specialist positions such as Data Analyst, Data Scientist, Industrial Research Scientist, Consultant in various industry sectors (Сhemical and Pharmaceutical industry, Oil & Gas, IT, Finance, and others).
      Landing PhD positions and continuing research at leading Russian and international research bodies.
      Starting a business on their own or through the Skolkovo innovation ecosystem with its extensive pool of experts, consultants and investors.
      Entry requirements:
      Knowledge and skills:
      Calculus, Differential Equations, Linear algebra, Probability theory and mathematical statistics, Numerical methods. High level of English proficiency.

      Programming skills: C/C++, Fortran, MATLAB, Python, Julia (at least one of the mentioned languages).
      Bachelor's degree or equivalent in Mathematics, Computer Science, Physics, Chemistry, or Engineering.
      English Language:
      If your education has not been conducted in English, you will be expected to demonstrate evidence of an adequate level of English proficiency.
      • Your CV (ENG)
      • Motivation letter (ENG)
      • 2 recommendation letters
      • Diploma or transcript
      • Your certificates and awards, achievements and other materials for portfolio
      Prepare your portfolio
      Prepare your application materials.
      Submit your application
      Upload your materials into the application system and submit your application.
      Online testing
      Every candidate must take an online profile test. You will be notified by email about the specific date and time of your test.
      In-person interviews (online)
      The final admissions stage will take place online this year. You will need to pass an interview with the admissions committee, participate in an entrepreneurship challenge and take an English language test. Extra written examinations may be required for certain programs during this time (you will be notified in advance).
      Exam samples:
      Keep in mind that the structure of exam can be changed
      following the decision of program committee.
      what our students say
      Dilyara Baymurzina
      BSc, Moscow Institute of Physics and Technology → MSc, Skoltech → Neural Networks and Deep Learning Laboratory, MIPT
      In the ACS program, I definitely learned a lot of different applications of the knowledge which is usually taught only theoretically at other universities. I believe studying in such an intense master's program is much more useful for students' futures than studying some theoretical subjects and working in parallel.
      Mahmud Allahverdiyev
      BSc, Qafqaz University → MSc, Skoltech → Snowflake
      During the HPC course, we have got a thorough understanding of how large-scale Big Data & AI applications are tackled in scientific and industrial settings. Hands-on practice assignments on frameworks such as OpenMP, MPI, CUDA will be helpful to you while working with HPC clusters & supercomputers for your research projects and potential future career in HPC. If you are particularly interested in parallel programming, HPC and distributed systems, don't miss the chance to check out the course.
      The program has a globally renowned faculty with international experience
      and a broad network of collaborations:
      The application period for Skoltech Master's programs for the 2021-2022 academic year is now closed. Subscribe below to receive a notification as soon as the application opens in the Fall.
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      phone: +7 (495) 280-1481_ext.3387
      address: 30с1 Bolshoi boulevard, Skolkovo, 121205, Russian Federation, Room E-R3-2030

      We are more than happy to meet visitors Monday to Friday from 9:00 to 18:00. Please arrange a visit 48 hours in advance by contacting