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, with knowledge of modern computing architectures, programming, code optimization, and large-language models HPC track students 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 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