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.

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