Mathematics and Computer Science

Program overview

This MSc is an interdisciplinary, English-language programme that gives equal weight to mathematics and computer science. Course work is structured so you can link the two areas directly through seminars and hands-on project modules, combining theoretical foundations with practical programming experience. Teaching emphasizes application-oriented training and offers close, individual support — including one-to-one supervision.

Recent advances in both disciplines mean they increasingly overlap. Examples include mathematics-driven algorithm work (optimisation, statistics and data science, scientific and high-performance computing, image processing) and computer-science topics that depend on strong mathematical foundations (machine learning, computer vision, process mining, quantum computing). The curriculum is designed to reflect these connections and prepare students to work at the intersection of the fields.

The programme is aimed at graduates with a Bachelor’s degree in mathematics, computer science or a closely related interdisciplinary field. Applicants must demonstrate a solid grounding in the fundamental concepts of both disciplines as part of the application. Graduates leave with a balanced mix of mathematical theory and programming project experience, positioning them well for advanced research or applied roles and offering strong career prospects in Germany and abroad.

Requirements (concise)

• Bachelor’s degree in mathematics, computer science or a closely related interdisciplinary field
• Demonstrable, solid foundational knowledge in both mathematics and computer science (must be documented in the application)
• Programme language: English

Curriculum overview

This interdisciplinary Master’s blends advanced mathematics with computer science, aiming to develop both theoretical understanding and hands‑on technical skills. The programme is split into distinct module groups so you build a balanced profile across modelling and optimisation, algorithmic and data‑driven computing, and independent research. Elective options allow you to steer the degree toward more pure mathematical theory or applied, algorithm‑centred work.

Key modules and learning outcomes

The mathematics component emphasizes applied and algorithmic mathematics—topics such as mathematical modelling and optimisation—while still offering a broad selection of elective courses in both pure and applied mathematics. Learning outcomes include the ability to formulate and analyse mathematical models, apply optimisation techniques, and connect rigorous theory with computational implementations.

On the computer science side, the curriculum focuses on subjects informed by a mathematical background: machine learning, deep learning, data science and quantum computing feature prominently. Lab‑based courses (for example, an Isabelle Lab or an Embedded Hardware Lab) provide practical experience in formal methods and hardware‑oriented systems. Graduates will be able to design and implement algorithmic solutions, apply statistical and learning methods to complex data, and engage with emerging computational paradigms.

Research, application and thesis

The programme includes two seminars (one in mathematics and one in computer science), an application module that exposes you to real‑world uses of both disciplines (e.g., computational materials science), and a scientific project carried out under one‑to‑one supervision. These components develop presentation, scientific writing and project management skills and prepare you for independent research. The master’s thesis (25% of the programme) is the capstone, consolidating research competence and subject‑specific expertise.

Requirements and structure (at a glance)

• Mathematics module group: 27% — applied/algorithmic focus, wide elective choice in pure and applied maths
• Computer Science module group: 27% — mathematically grounded CS topics (machine learning, deep learning, data science, quantum computing); optional lab courses such as Isabelle Lab and Embedded Hardware Lab
• Seminar / Application Module / Scientific Project Work: 21% — mathematics seminar, computer science seminar, application module (e.g., computational materials science), and a 1:1 supervised science project
• Master’s thesis: 25% — independent research project forming the programme’s final assessment

Graduates are well placed for roles that require strong quantitative and programming skills, such as data scientist, machine learning engineer, algorithm developer, computational scientist, or software engineer in sectors like finance, engineering, automotive, IT and research labs. The combination of mathematical foundations and practical project experience also provides a solid basis for continuing to doctoral studies (PhD) or pursuing research-oriented careers.

Careers are possible both in Germany and internationally; the programme’s applied orientation and lab/project components help graduates transition quickly into industry roles, startups or academic research groups.