Master of Science in Data Science at the University of Münster - Profile

The University of Münster is introducing a new interdisciplinary Master’s program in Data Science, scheduled to commence in the Winter Semester 2026/27. The program provides a specialized foundation in the methodology of Artificial Intelligence (AI) and Machine Learning (ML), with a strong focus on their application within the natural sciences.

Interdisciplinary Tracks and Specializations

The program is hosted at the Faculty of Mathematics and Computer Science. In close collaboration with four different faculties (Mathematics & Computer Science, Physics, Chemistry & Pharmacy, and Geosciences), the curriculum is uniquely designed to offer both rigorous methodological and technical depth and broad natural science expertise. This interdisciplinary structure allows students to tailor their studies to their individual interests, choosing between high-level specialization in AI methodology or the application of data-driven solutions within diverse scientific domains.

The program bridges the gap between theoretical AI research and domain-specific challenges. Depending on their background and interest, students specialize in one of the following areas:

The programme spans Mathematical and Computational Data Science, focusing on the theoretical, algorithmic, and numerical foundations of AI and machine learning, alongside its application in Physics, Chemistry, and Pharmaceutical Sciences, where data-driven methods support modeling, simulation, experimental analysis, and innovation in domain-specific research.

• Mathematical and Computational Data Science

   

  The Mathematical and Computational Data Science track provides an in-depth, methodologically rigorous education in the theoretical and algorithmic foundations of Data Science and Machine Learning. Designed for students with a strong background in mathematics, computer science, or related disciplines, this track emphasizes advanced statistical modeling, optimization, numerical methods, algorithmic design, and the formal analysis of learning systems. Students develop a deep understanding of the mathematical structures underlying modern AI, enabling them not only to apply existing methods but also to critically evaluate, adapt, and further develop machine learning algorithms. Through advanced coursework, research-oriented seminars, and a method-focused Master’s thesis, graduates are prepared for careers in high-level research and development in academia or industry, particularly in areas where robust, interpretable, and theoretically sound AI methods are essential.

• Data Science in Physics

  The Physics Data Science track offers a comprehensive education in the application of data science methods to complex physical systems, combining theoretical insight with experimental and observational practice. Aimed at students with a strong background in physics, geophysics, or related disciplines, this track integrates advanced training in scientific computing, data-driven methods, and physics-informed machine learning. Students develop the ability to model, analyze, and interpret large and complex datasets arising from physical experiments, simulations, and Earth observation systems, while maintaining a strong connection to underlying physical principles. Emphasis is placed on bridging domain knowledge with modern AI techniques, enabling students not only to apply existing tools but also to design innovative, physically consistent models. Through specialized coursework, interdisciplinary projects, and a research-oriented Master thesis, graduates are well prepared for careers in scientific research, high-tech industry, and data-intensive fields where combining physical understanding with advanced analytics is crucial.

• Data Science in Chemistry

  Data Science and AI techniques play a rapidly increasing role in essentially all areas of chemistry, ranging from fundamental applications in quantum chemistry via machine-learning models for material simulations, property prediction, and synthesis planning, up to generative AI models for compound design or automated extraction of context-specific data from the literature. The Chemistry Data Science track is designed for students with a BSc degree in Chemistry who want to combine their strong background in Chemistry with rigorous training in data analysis and modeling, scientific programming, and machine learning for advanced chemical problems. Courses in Data Science, Mathematics, Computer Science, and advanced Chemistry subjects are the main pillars of this MSc track. They form the basis for an interdisciplinary Chemistry Data Science project and a research-oriented Master’s thesis, in which students apply modern data-driven methods to current research questions in chemistry. Trained in this way, graduates acquire a strong quantitative skill set combined with domain expertise in chemistry, providing an excellent basis for careers both in academia and in industry, where data-driven approaches and AI are increasingly essential for solving important problems in molecular and materials science. 

• Pharmaceutical Data Science

Curriculum Structure

To support this high level of specialization, the program utilizes a modular structure:

Foundation Phase: Specialized modules in Mathematics, Computer Science, and Natural Sciences ensure all students possess the necessary prerequisites for advanced AI studies.

Project-Based Learning: An interdisciplinary project in the third semester allows students to apply ML algorithms to real-world research problems in collaborative cross-faculty teams.

Research Thesis: The program culminates in a Master’s thesis where students independently solve a scientific problem using advanced data science and AI methodologies.

Key Facts

Degree: Master of Science (M.Sc.).

Duration: 4 Semesters (120 ECTS).

Language: English.

Admission: Requires a Bachelor’s degree in a relevant field (e.g., Mathematics, CS, Data Science, Physics, Chemistry, Pharmacy) and English proficiency at the B2 level.