• Scientific and pedagogical direction - Master of Natural Sciences

ON 1 Develop and use algorithms for computational research and experiments using modern effective methods of computational mathematics

ON 2 Develop methods and algorithms for computational mathematics based on the approximation of differential equations by methods of finite differences, volumes or elements

ON 3 Conduct a fundamental analysis of computational methods and difference schemes for convergence and correctness, including in the case of high-performance algorithms using elements of mathematical logic and the theory of computability

ON 4 Solve computational problems with complex geometry of regions by building and using correct structured, curvilinear, unstructured computational grids

ON 5 Use deep learning-based data mining techniques, reinforcement learning to adapt the computational algorithm to efficiently predict outcomes

ON 6 Conduct classes in mathematical disciplines offline and online based on innovative technologies, developing methodological recommendations for independent student work, laboratory, practical, lecture courses, textbooks, teaching aids, educational work plans, etc.

ON 7 Develop parallel computing algorithms for engineering problems and implement them in high-performance systems, develop quantum computing algorithms.

ON 8 Develop and conduct computational simulations of probabilistic processes from various industries using stochastic analysis methods and stochastic differential equations

ON 9 Use the methods of mathematical statistics in various areas of the economy and production, on real data for the selection of parameters, adaptation and testing of computing systems based on real experiments

ON 10 Conduct independent scientific research, solving modern urgent problems, publishing results in rating journals and speaking at conferences