about the programme
In the Applied Mathematical Stochastic Methods master’s study programme, students develop their analytical thinking and learn to work with data, advanced statistical tools, and mathematical modelling. Emphasis is placed on the practical use of these methods—from capacity estimation and simulation of traffic systems, to the detection of hidden defects in materials, and to the application of machine learning methods across a wide range of real-world problems, whether in scientific research or in the commercial sector.
programme content
Students will attend core courses focused on:
- machine learning,
- advanced and robust regression models,
- heuristic algorithms.
career prospects
Graduates are capable of applying their acquired knowledge across a wide range of fields—from physics and engineering to socioeconomic modelling. They can pursue careers in scientific research, banking, IT, traffic engineering, or data science. They participate, for example, in the development and application of digital image processing methods, the analysis of particle accelerator data, defectoscopy tasks, simulations of pedestrian or vehicle movement, or the evaluation of the reliability of technological systems.
state final exam
Compulsory subject
Methods of Regressive Analysis
Optional subjects I
Information Theory and Random Processes
Machine Learning
Optional subjects II
Reliability and Extreme Events
Mathematical Models for Traffic Flow