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.
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.
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
Aim and objectives
Data-Intensive Mathematical Modelling and Simulations (DIMMS) Track
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 Track
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 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.
36 credits
compulsory and recommended elective courses
36 credits
Research and MSc thesis project
24 credits
Elective courses and projects
12 credits
Entrepreneurship and innovation
12 credits
Industrial immersion
Research
Students are actively involved in research activities starting from Term 3.
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
Career opportunities and paths
Industry
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).
Science
Landing Ph.D. positions and continuing research at leading Russian and international research bodies.
Startup
Starting a business on their own or through the Skolkovo innovation ecosystem with its extensive pool of experts, consultants, and investors.
Entry requirements
Bachelor’s degree or equivalent in Mathematics, Computer Science, Physics, Chemistry, or Engineering.
Knowledge and skills:
Calculus, Differential Equations, Linear algebra, Probability theory and mathematical statistics, Numerical methods.
English language requirements:
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
Application requirements
The online application​ makes the process easier for potential students. We advise you carefully read the application instructions, requirements, and deadlines for the chosen academic program.
The application includes the following documents: a CV, two letters of recommendation, a TOEFL/IELTS score report, and a motivation letter. Applicants who do not have proof of English proficiency may take the TOEFL ITP during a Selection Weekend at Skoltech.
Selection process
Prepare your portfolioPrepare your competitive selection application materials.
Submit your applicationUpload your materials into the application system and submit your application.
Online testingEvery 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 selection stage takes place in Moscow. You have to pass the TOEFL ITP exam on-site, or present a valid TOEFL certificate and pass an in-person interview. Extra written examinations may be required for certain programs during this time (you will be notified in advance).
What our students say
Dilyara BaymurzinaBSc, 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 AllahverdiyevBSc, 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."
