This master's programme trains you to use numerical simulation and data-driven methods to solve complex problems in science and technology. As industry and research increasingly replace physical prototyping with computer-based modelling, demand has grown for more accurate models and for methods that incorporate large-scale data and uncertainty (stochastic models). Scientific computing brings these themes together: modelling, mathematical and statistical analysis, numerical solution of differential and integral equations, optimisation, high-performance implementation and visualization.
The curriculum covers the entire computational workflow. You will deepen your mathematical understanding of problems, learn modern numerical methods and data-analysis techniques for large datasets, and gain practical experience writing and optimising numerical software for high-performance computers. Course content is applied in interdisciplinary modelling seminars where well-known industrial partners present real-world problems and stay involved throughout the solution process, giving hands-on experience with industry-relevant challenges.
This international, English‑taught Master of Science is aimed at students working at the intersection of mathematics, computer science and application domains. The programme provides a focussed, mathematically rigorous education while remaining accessible to motivated students from related disciplines. As an elite degree programme funded by the state of Bavaria, it offers special financial support and intensive supervision; exceptionally strong students may also combine the master’s with a fast‑track route into doctoral research.
Requirements (summary)
The programme combines mandatory and elective modules: students choose from a range of elective courses to reach the required number of credit points while also completing core, compulsory modules. Teaching and research concentrate on four core pillars — numerical mathematics (numerical methods for various differential equations, approximation techniques, optimisation), modelling and simulation (applications across physics, biophysics, bioinformatics, chemistry, engineering and climate/environmental sciences), high-performance computing (data structures, parallel systems and algorithms), and advanced scientific computing topics (complexity reduction, fast and efficient methods, mesh-free techniques, data analysis, uncertainty quantification, multiscale problems, and optimisation methods used in machine learning).
A modelling seminar (offered in the summer semester) and a status seminar (offered in the winter semester) run each year; students are required to attend two instances of each. Practical experience is emphasised through an industrial internship and a hands-on course in parallel numerical methods to deepen algorithmic and implementation skills. There is also a dedicated key-skills module covering presentation and lecture techniques, literature research, teamwork and working with foreign-language specialist texts; attendance at seminars in this module is required for a set amount of time.
Learning outcomes include the ability to design and implement robust numerical methods for differential equations, build and validate models across scientific disciplines, exploit parallel and high-performance computing architectures, apply modern complexity-reduction and multiscale techniques, perform data analysis and uncertainty quantification, and use optimisation approaches relevant to machine learning. The programme culminates in an individual Master’s thesis carried out in cooperation with industry, international experts or under the supervision of a university professor; students receive compensation for travel expenses during research stays. Each student is paired with a mentor (selected from involved lecturers) to help shape an individual study plan and advise on research and thesis topics. The modules and course structure were developed in cooperation with the Elite Network of Bavaria.
Requirements and key components
You must hold a relevant undergraduate degree and a solid foundation in numerical mathematics to be eligible. Acceptable first degrees include mathematics, computer science, engineering science or physics — or another degree that contains equivalent subject matter. The program expects a strong academic result and demonstrable coursework in numerical methods.
Specifically, applicants need a final grade of 1.9 or better (as stated by the programme) and at least 16 credits of specialised coursework in numerical mathematics. Numerical mathematics typically covers topics such as numerical analysis, numerical linear algebra, scientific computing and related computational methods; courses in these areas are what the programme counts toward the 16-credit requirement.
Admission requirements (bullet points)
Winter Semester (International)
15 May 2026
Summer Semester (International)
15 November 2026
Winter Semester (EU/EEA)
15 May 2026
Summer Semester (EU/EEA)
15 November 2026
Graduates are prepared for roles that require advanced modelling and numerical skills: positions in R&D, simulation and software development teams in engineering, aerospace, automotive, energy and climate science sectors; specialist roles in high-performance computing centres; and data- or numerical-analysis roles in finance and industry. The programme’s strong mathematical and computational core also provides a direct pathway to PhD study and research careers.
Thanks to the combination of theoretical foundations, HPC skills and industry projects, alumni typically move into jobs developing large-scale simulation software, optimisation and uncertainty-quantification pipelines, or into interdisciplinary research groups that bridge academia and industry.
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