Mathematics

Program overview The Master of Science in Mathematics is a four-semester, English‑language programme designed for students who hold a Bachelor of Science in Mathematics or an equivalent degree (for example, in statistics). The curriculum combines deep mathematical training with a minor subject that makes up roughly 30% of the programme. Two minor tracks are offered: Natural sciences and technology (examples: electrical engineering, computer science, mechanical engineering, physics) or Business mathematics (economics or business information systems).

Learning outcomes and career paths The degree strengthens advanced mathematical competencies and oral/written communication, preparing graduates to carry out scientific work, teach, and collaborate in interdisciplinary teams. You will learn to analyse abstract structures, formulate and solve mathematical problems, and apply those skills within your chosen minor. Graduates can continue into doctoral research (PhD) or enter a broad range of industries. Typical professional applications include risk forecasting for insurance and finance, improving encoding and cryptographic techniques (online banking, communications, multimedia), route optimisation for transport systems, development of mathematical models in automotive, aerospace, steel or medical sectors, and processing and evaluation of large data sets in banks and insurers.

Master’s thesis The programme culminates in an independently written Master’s thesis under regular supervision. The thesis topic is agreed in advance with a supervisor and may be drawn from any area of mathematics; industry co‑supervision is possible.

Requirements (key facts)

• Intended entrants: holders of a BSc in Mathematics or an equivalent discipline (e.g., statistics)
• Duration: 4 semesters
• Language of instruction: English
• Two available minors: Natural sciences and technology; Business mathematics
• Minor constitutes approximately 30% of the programme
• Programme ends with a Master’s thesis (independently written, supervised; industry co‑supervision possible)
• Completion allows progression to doctoral studies (PhD)

Program structure and modules

Each semester the Department of Mathematics runs a broad selection of lectures and seminars across four core areas: algebra, analysis, stochastics and optimisation. Students are free to choose from these offerings rather than following a rigid sequence, allowing you to shape your study profile toward pure or applied directions. Seminars complement lectures by giving space for discussion of current research topics and for developing presentation and project skills.

What you'll learn

Through advanced courses in algebra and analysis you will deepen your theoretical understanding and gain higher-level proof techniques and abstraction skills. Stochastics modules focus on probabilistic modelling and methods for uncertainty, while optimisation courses develop analytical and computational tools for finding best solutions in constrained settings. Overall the curriculum trains you to read and work with contemporary research literature, tackle complex mathematical problems independently, and communicate mathematical ideas clearly—preparing you either for doctoral study or quantitatively demanding careers.

Practical guidance for international students

There is no universal order in which to take modules, but many lectures naturally assume prior knowledge. Each module’s description lists any necessary prerequisites, so check the module handbook and speak with an academic advisor when planning your semesters to ensure you meet requirements and build an appropriate progression through the material.

Key facts / requirements

• Courses and seminars are offered every semester in algebra, analysis, stochastics and optimisation.
• There is no fixed sequence of modules; you may mix and match across semesters.
• Some lectures presuppose prior knowledge; prerequisites are specified in each module description.
• Consult the module catalogue and an academic advisor before enrolling to ensure you meet listed prerequisites.

Graduates are prepared for roles in industry and finance that require strong analytical and modelling skills. Typical positions include risk forecasting and financial data analysis in banks and insurance companies, development and improvement of encoding and cryptographic techniques, optimisation and logistics roles (e.g., route planning for transport), and development of mathematical models in sectors such as automotive, aerospace, steel and medicine. The programme’s emphasis on data processing and evaluation also suits careers in data science and analytics.

The degree also provides a solid foundation for academic paths: the research orientation and taught competences enable graduates to pursue doctoral studies (PhD) or academic and teaching positions. The option to undertake a Master’s thesis with industry co-supervision supports transitions into applied research and R&D roles in private-sector organisations.