Overview AIMS is an interdisciplinary, research-focused Master’s programme taught entirely in English at TU Braunschweig. It is designed for an international student body and combines molecular science with modern data-science methods, especially artificial intelligence and machine learning. The course aims to train students to use computational approaches to address molecular-level problems in chemistry and biology.
Students choose one of three specialised study tracks—Chemical Synthesis and Drug Design, Spectroscopy and Imaging, or Data-Driven Biology—and learn to build and apply AI models to accelerate physical simulations and to process and interpret large, high-dimensional datasets describing molecular structure and dynamics. The programme’s research orientation means students will engage with current methods and problems at the intersection of molecular science and AI.
The international focus creates a multicultural learning environment that supports collaboration across disciplines and backgrounds. Graduates leave with a blend of theoretical knowledge and practical data-science skills applicable to academic research and industry roles working on molecular modelling, drug discovery, imaging analytics, and related fields.
Key facts / programme features
Curriculum overview
This research-focused Master's programme builds on your Bachelor's background by deepening core skills in applied mathematics and computer science while adding specialist knowledge in molecular sciences. Teaching is designed to be interdisciplinary and research-oriented: you will learn mathematical modelling, algorithmic and computational techniques, and how to apply these tools to problems in chemistry, biology and related molecular disciplines. The programme emphasises independent scientific work and prepares you to formulate and solve complex research questions.
Specialisations and study flexibility
You choose one of three specialist tracks to tailor the degree to your goals:
The modular structure lets you combine your specialisation with complementary subjects, take part in cross-faculty options with the Faculty of Life Sciences, draw on major research facilities in the Braunschweig research region, and pursue study or research periods at foreign partner universities. Course offerings at TU Braunschweig provide a broad selection of modules to shape your individual study plan.
Key modules and learning outcomes
Core modules focus on applied mathematics and computer science fundamentals that underpin AI applications (modelling, numerical methods, machine learning, algorithm development). Specialisation modules cover domain-specific experimental and analytical methods—such as synthetic strategies and drug design concepts, spectroscopy and imaging techniques, or computational biology and data-driven approaches. Throughout the programme you will engage in project-based learning and supervised research, culminating in an independent Master's thesis written and presented in English. Graduates leave able to design and carry out interdisciplinary research, apply AI and computational methods to molecular problems, critically evaluate scientific results, and communicate findings in an international research environment.
Study requirements (curriculum-level)
(Refer to the programme study plan PDF for the detailed module catalogue and course sequencing.)
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Winter Semester (International)
15 March 2026
Winter Semester (EU/EEA)
15 July 2026
Graduates are prepared for roles at the interface of molecular science and data science, including positions in computational chemistry, drug discovery, biotechnology, pharmaceutical industry, and companies applying machine learning to molecular and materials problems. The programme’s research focus also provides a strong foundation for continuing to doctoral studies or research careers in academia and research institutions.
Skillsets developed—such as building AI/ML models for simulations and analysing high-dimensional molecular data—are relevant to industry R&D teams, startups in computational life sciences, and multidisciplinary teams in data-driven biology and imaging.
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