• Scientific and pedagogical direction - Doctor of Philosophy (PhD)

1.To organize training sessions taking into account the principles of student-centered learning and assessment, teaching materials in the disciplines taught, integrating education, science and innovation, as well as providing feedback to students using digital technologies.
2.To conduct scientific research and obtain new fundamental and applied results, critical analysis and evaluation of the data obtained, formulation of sound conclusions even in conditions of incomplete or limited information, as well as participation in research and development/creative projects, increasing scientific effectiveness and publication activity, working with national and international databases.
3.To organize pedagogical interaction, mastering the integration of psychological and pedagogical knowledge and the subject area, mentoring young teachers, familiarizing themselves with regulatory legal acts and modern teaching technologies.
4.To increase the civic and professional activity of students, observing the principles of academic honesty and integrity, forming a steady interest in the chosen profession.
5.To write scientific articles in foreign and domestic scientific journals and inform the general scientific community about advanced topics and research results at international and national conferences, seminars and workshops, critically assessing their significance.
6.Independently write scientific projects and applications, setting a theoretical or practical calculation task or solution method relevant to society, implement and adjust, if necessary, the process of independent scientific research.
7.To conduct scientific research in the field of methodology of computational experiments based on approximation of differential equations by methods of finite differences, volumes and/or elements.
8.To carry out a fundamental analysis of computational methods and difference schemes for convergence and correctness, including in the case of high-performance algorithms.
9.Create and use correct structured, curvilinear, and unstructured computational grids in computational problems.
10.Develop parallel computing algorithms for engineering problems and implement them in high-performance systems, develop quantum computing algorithms.
11.Use methods of mathematical statistics based on real data to select parameters, adapt and test computing systems based on real experiments.
12.Use deep learning and data mining techniques to effectively predict research outcomes.