The track aims to foster a new generation of computational scientists and engineers capable of combining first-principles and data-driven approaches, including AI-based approaches to mathematical modeling of natural, industrial, and social phenomena. The curriculum carefully balances advanced computing, artificial intelligence, and computational physics to implement multiscale models in modern computational environments.

A graduate of the track is well-positioned 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, exploit the existing special software solutions and their combinations for computationally demanding and data-intensive modeling and simulations
• apply relevant computational approaches, including methods of AI and data science, hardware, and software for the solution of complex real-world problems

The modern computational world is essentially parallel, as CPUs and GPUs contain multiple cores. Datasets and computational problems are becoming impossible to process 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 land 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 present-day computing challenges using 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 code
• support efficient infrastructures for HPC clusters, big data applications, and data centers