Mathematical Modelling, Programming and Artificial Intelligence
Source: https://english.spbu.ru/admission/programms/graduate/mathematical-modelling-programming-and-artificial-intelligence Parent: https://english.spbu.ru/admission/programms/graduate
01.04.02 In Russian
Level of education Master
Type of instruction Full-time
Duration 2 years
Programme description
- The programme is designed for applicants with bachelor’s degrees and for students with different levels of training in mathematics.
- It is based on individual educational trajectories, aimed to meet employers' requirements for employee qualifications to the maximum extent possible. This enables graduates of the programme to: acquire the knowledge, skills and abilities they need; and start work with a minimum adaptation period.
Our advantages
- The degree programme is aimed at training experts who are capable of: independently setting and solving theoretical and practical tasks in this and related subject areas; independently conducting research in areas that use methods of applied mathematics and computer technology; developing and using mathematical models of processes and objects; developing and applying cutting-edge mathematical methods and software to solve problems of science, technology, economy and management; and applying effective mathematical approaches in the field of artificial intelligence systems and modelling of complex systems.
- Students will learn how to: formalise the original problem; construct mathematical models and verify their adequacy; develop known and create new methods of solving mathematical physics problems; apply methods of paralleling calculations to solve practical problems; create a computer implementation of the resulting solutions; use effectively statistical and mathematical packages; create effective computer realisations of statistical methods for solving practical problems; and develop known and create new statistical methods of data processing.
- Graduates of the programme will be capable of carrying out research, design, engineering and manufacturing, organisational and managerial, and teaching work related to: the use of mathematics, programming, information and communication technology and automated control systems; mathematical and statistical modelling; and the development of software for science and industry.
Career opportunities
Professions
- Analyst
- Statistician
- Analyst-programmer
- Software engineer
- Teacher of vocational education, and lifelong professional education and training
- Information systems specialist
- Research associate
Main courses
- Algebraic Problem of Eigenvalues and Solution of Mathematical Physics Problems
- High-Performance Computing and Paralleling
- Parallel and Distributed Computing
- Computer Convex Programming Methods
- Fundamentals of Multi-Body System Dynamics
- Graph Theory
- Artificial Intelligence
- Mathematical Modelling of Cybernetic and Robotic Systems
- Introduction to Cybernetic Systems Theory
- Frequency Methods of Nonlinear System Analysis
- Computer Vision Algorithms
- Autonomous Robot Navigation and Motion Control
- Machine Learning
- Programming the Robots
- Artificial Neural Networks and Big Data Processing
Main areas of research
- Theory and applications of randomised quasi-Monte Carlo methods (research supervisor: Professor Sergei Ermakov)
- Deterministic and chaotic dynamics of synchronisation and control systems (research supervisor: Professor Nikolay Kuznetsov)
- Analytical and numerical methods and artificial intelligence in the analysis of multi-stability and hidden oscillations of dynamic systems (research supervisor: Professor Nikolay Kuznetsov)
- Control of mechatronic and robotic systems under limited resources (research supervisor: Professor Alexey Matveev)
- Oscillations and stabilisation of systems with pulse modulation (research supervisor: Professor Alexander Churilov)
- Development of mathematical methods for optimal planning and analysis of regression models (research supervisor: Professor Viatcheslav Melas)
- Development of mathematical methods for the analysis and forecasting of onedimensional and multidimensional time series within the framework of the singular spectrum analysis (research supervisor: Associate Professor Nina Golyandina)