Master's in Mathematics (MSc)

This Master's programme is research-oriented and offers advanced, in-depth study across a broad spectrum of pure and applied mathematics. Teaching and supervision come from faculty who are internationally recognised experts in areas such as Algebra and Geometry; Analysis and Differential Equations; Numerical Analysis and Partial Differential Equations; Scientific Computing; Inverse Problems; Computational and Data Sciences; Computational Statistics; and Stochastics (including stochastic processes in finance, actuarial science and engineering). The curriculum is designed to be flexible, allowing you to focus on either theoretical or application-driven tracks depending on your interests.

You can also complement your mathematics studies with courses outside the department in fields like Computer Science, Physics, Economics, Mechanical Engineering and Electrical Engineering, which helps prepare for interdisciplinary research or industry roles. The programme is closely connected to several research centres within KIT, including Collaborative Research Centre CRC1173: Wave Phenomena — analysis and numerics; KIT Center MathSEE: Mathematics in Sciences, Engineering, and Economics; and Research Training Group RTG2229: Asymptotic Invariants and Limits of Groups and Spaces, providing opportunities to engage with active research projects.

Teaching formats include lectures, research and student seminars that expose you to current topics, train you to read recent literature critically and present results to an audience. The degree culminates in a required Master’s thesis, through which you carry out a substantial research project and gain in-depth expertise in a chosen topic—excellent preparation for doctoral study or research-focused careers.

Program components and requirements

• Flexible course composition across pure and applied mathematics
• Option to take elective courses in related non-mathematical subjects (e.g., Computer Science, Physics, Economics, Mechanical and Electrical Engineering)
• Participation in research and student seminars to develop literature review and presentation skills
• Completion of a required Master’s thesis involving original research
• Close interaction with research centres such as CRC1173, KIT Center MathSEE, and RTG2229 for research opportunities

Curriculum overview

The MSc program requires 120 credit points (CP) to be completed over four semesters. In the first three semesters students take primarily lectures and seminars totaling 90 CP, choosing courses that match their interests across core areas: pure mathematics (for example algebra, analysis, geometry), applied mathematics (for example partial differential equations, numerical analysis, stochastic processes, inverse problems, scientific computing), and selected non-mathematical subjects (for example computer science, physics, economics, mechanical engineering, electrical engineering). The final semester is dedicated to a research-focused Master’s thesis worth 30 CP, written under the supervision of a faculty member on a chosen topic.

Key modules and typical learning outcomes

• Pure mathematics modules (algebra, analysis, geometry) build rigorous proof skills, abstract reasoning, and a deep theoretical foundation.
• Applied mathematics modules (PDEs, numerical analysis, stochastic processes, inverse problems, scientific computing) develop modeling abilities, numerical and computational techniques, and experience with real-world problem solving.
• Interdisciplinary/non-mathematical electives (CS, physics, economics, engineering disciplines) provide complementary knowledge, programming and data skills, and the ability to apply mathematical methods in other domains.
• The Master’s thesis demonstrates independent research competence: formulating a research question, conducting literature review, applying appropriate mathematical methods, and communicating results in writing and (typically) orally.

Program requirements (concise)

• Total: 120 CP over four semesters
• Coursework: ~90 CP of lectures and seminars across the first three semesters, chosen from pure math, applied math, and approved non-math subjects
• Thesis: 30 CP Master’s thesis in the fourth semester, supervised by a faculty member

For the full study plan, detailed module descriptions and formal regulations, please consult the program’s StudyPlan webpage.

Graduates are well prepared for research careers (including progression to PhD programmes) as well as for quantitative roles in industry. The programme's strong foundations in numerical methods, scientific computing, statistics and stochastic modelling suit positions in data science, computational engineering, finance and actuarial practice. Employers in technology, engineering firms, finance and research institutes value the analytical, modelling and problem-solving skills developed in this MSc.

The interdisciplinary elective options and exposure to applied projects also make graduates attractive for roles that bridge mathematics with computer science, physics or economics—for example algorithm development, simulation and modelling, or analytics and risk assessment.