Master of Science in Computational Mathematics

Computational mathematics brings together rigorous mathematical theory and high-performance computing to tackle complex, real-world problems. If you enjoy spotting patterns and constructing precise solutions, this Master’s programme trains you to use numerical methods, algorithm design and simulation to address challenges across engineering, medical data science, logistics and other application areas. The curriculum emphasises how mathematical insight and computational tools combine to produce reliable, scalable solutions.

The programme places special emphasis on the mathematical underpinnings of modern computational techniques, including areas relevant to artificial intelligence. You will study numerical algorithms and models used for problems in deep learning, neural networks and related AI methods, learning how to develop, implement and analyse these approaches. Teaching is entirely in English and the course is designed to prepare you for both industrial roles and research-driven careers where strong mathematical and computational skills are required.

As an international student you benefit from a degree that foregrounds foundational mathematics while remaining closely connected to contemporary AI and data-driven applications. The programme’s international partnerships also create opportunities for study abroad and academic exchange, helping you build a global perspective and professional network.

Program highlights / requirements

• Maths meets AI: emphasis on mathematical approaches to AI-related problems
• Focus on mathematical foundations and numerical algorithms
• Fully English-taught Master’s programme
• Numerous partnerships with universities abroad for exchange and collaboration

Curriculum overview

The program combines a small set of core requirements with a wide choice of advanced elective modules so you can build a specialization in computational and applied mathematics. The core workload consists of two mathematics seminars plus the presentation of your Master’s thesis, which together ensure both depth in a chosen topic and experience in scholarly communication. Elective courses are organised into thematic module groups that let you tailor the degree toward theory, algorithms, modelling or probabilistic methods.

Elective focus areas and highlights

Compulsory elective modules are grouped by mathematical themes. Key offerings include algebraic topics such as cryptography and computational algebra and logic; analysis-oriented subjects like compressed sensing, the mathematical foundations of machine learning, approximation theory and information-based complexity; and application-driven courses covering signal and image analysis, mathematical modelling and (stochastic) simulations. Geometry courses range from expander and random graph theory to convex geometry, while optimisation includes linear and non-linear programming as well as computational game theory. Stochastics modules cover Bayesian inference, stochastic differential equations and Monte Carlo methods. This structure supports both rigorous theoretical training and the development of practical computational tools.

What you will gain

Through these modules you will develop a strong theoretical grounding in modern mathematical topics together with hands-on competence in numerical and probabilistic techniques. Expected outcomes include the ability to analyse and design algorithms for algebraic and optimisation problems, apply advanced analytical methods in signal, image and data contexts, formulate and simulate mathematical models (including stochastic models), and use probabilistic inference tools for uncertainty quantification. The seminars and thesis give practice in presenting results and conducting independent research.

Program requirements (concise)

• Core modules:
  • Two mathematics seminars
  • Presentation of the Master’s thesis
• Compulsory elective module groups (examples of topics):
  • Algebra: cryptography; computational algebra and logic
  • Analysis: compressed sensing; mathematical foundations of machine learning; approximation theory; information-based complexity
  • Applications: signal and image analysis; mathematical modelling; (stochastic) simulations
  • Geometry: expander and random graph theory; convex geometry
  • Optimisation: linear and non-linear programming; computational game theory
  • Stochastics: Bayesian inference; stochastic differential equations; Monte Carlo methods

Graduates are prepared for technical and analytical roles that require advanced mathematical modelling and algorithmic skills, including positions as data scientists, quantitative analysts, algorithm engineers, numerical simulation specialists, and research scientists in industry (e.g. engineering, medical data science, logistics, AI companies). The programme’s emphasis on foundations and computational methods also provides a strong basis for doctoral studies and careers in academic research. Practical experience via voluntary internships (recommended and supported by university advisory services) and international partnerships can further enhance employability and opportunities abroad.