This research-focused, interdisciplinary and international master’s programme brings together expertise from multiple faculties to train students in both the theory and practice of computational modelling and computer simulation. The curriculum covers mathematical and algorithmic foundations—such as learning models from data, inverse problems and artificial intelligence—alongside numerical simulation and logical approaches to forward problems. Teaching and project work take place across a range of participating faculties, creating an environment well suited to students who want to work at the intersection of disciplines.
Students choose an application-specific specialisation while following a shared, application-independent core. Available specialisation tracks are:
The programme is modular and emphasizes transferable, long-lasting skills through core modules, cross-track seminars and a hands-on interdisciplinary research project. It also provides an option for a fast-track into a structured PhD pathway, leveraging local research partnerships including participation in an international Max Planck graduate programme. The standard study period is four semesters: three semesters of coursework followed by one semester devoted to the Master’s thesis.
Key facts and structure
Overview
This interdisciplinary Master’s curriculum is built from modules that combine a common, application-independent core with specialised, application-specific tracks. Students pick one track at application and progress through four semesters of taught modules, seminars and research work. Teaching formats include lectures, practical exercises, seminars, tutorials, supervised research projects and language courses.
Key modules and what you learn
Learning outcomes (what you will be able to do)
Module and credit breakdown (concise)
Available foundational modules (choose three in semester 1)
This master's programme requires a completed first university degree that qualifies you for professional work in one of the following areas: Computer Science, Mathematics, a Natural Science, Economics/Finance, or Engineering. Acceptable prior degrees include BSc (including BSc Hons), MSc, the German "Diplom" or equivalent bachelor/master-level qualifications. The course builds on core computer science and mathematical foundations, so applicants should already be familiar with the basic concepts listed below.
In addition to the mandatory foundations, prior coursework in related advanced or specialised subjects can be beneficial but is not mandatory. These recommended topics give you a head start in the programme’s modelling and simulation focus, but missing them does not automatically disqualify an applicant.
Mandatory prerequisites (must be demonstrated)
Recommended (helpful but not required)
Winter Semester (International)
31 May 2026
Winter Semester (EU/EEA)
15 July 2026
Graduates are prepared for technical and research roles that require strong modelling, simulation and data-driven problem-solving skills. Typical employers include research institutions, tech companies, engineering firms, computational biology and life-science organisations, energy and finance companies, and visual computing or AI-focused enterprises. The programme’s emphasis on fundamentals and interdisciplinary projects also makes graduates competitive candidates for PhD programmes and further academic research.
Students who choose the fast-track research option and maintain strong ties with local research partners may move directly into structured doctoral training; others typically enter industry roles such as computational scientist, simulation engineer, data scientist, machine learning engineer or specialist in domain-specific modelling teams.
Trier University of Applied Sciences — Birkenfeld
Technische Universität Braunschweig — Braunschweig
Furtwangen University — Villingen-Schwenningen
University of Siegen — Siegen