Overview The Computational Materials Science (M.Sc.) program trains students to combine deep knowledge of natural and materials sciences with strong skills in scientific programming, simulation and data analysis. Graduates are prepared to play central roles in industrial R&D and academic research, contributing to the design, processing and application of advanced materials with improved mechanical, thermodynamic and electronic performance.
Teaching approach At TU Bergakademie Freiberg the program brings together researchers and lecturers from several disciplines to link theoretical foundations with state-of-the-art research projects. You begin with an intensive seminar that brings you up to speed on scientific programming and modern computing environments, and you will participate in research seminars that include interaction with leading scientists and industrial partners. A distinctive feature of the course is hands-on tutorials where you learn to apply contemporary simulation methods; elective modules allow specialization in computational engineering/mechanics or in data-driven materials topics such as data mining and machine learning.
What you will learn The curriculum covers both theory and practical application of a wide range of numerical and simulation techniques, including:
Admission & requirements
For more details, visit: https://cms-tubaf.de/
This two-year (minimum four-semester) full-time MSc program begins in the winter semester and is designed to take students from a common foundation to independent research and professional practice in computational materials science. The curriculum deliberately levels all entrants during the first semester by consolidating core knowledge in numerical analysis, mechanics of materials, and programming. Incoming international students also receive support to improve their German language skills—useful for daily life, academic integration, and cultural orientation in Germany.
The second semester centers on computational simulation methods: you will learn the theoretical and numerical principles behind common approaches, understand their strengths and limitations, and gain hands-on experience with dedicated simulation tools. This semester also introduces contemporary Machine Learning and AI-assisted programming techniques and shows how these methods can accelerate modelling and project development. In the third semester you can choose from a range of electives in materials science and advanced mechanics while further honing your software skills through a Personal Programming Project—an applied assignment that requires you to solve a materials-, mechanics- or machine-learning-related problem using the methods and programming practices learned earlier.
The final (fourth) semester is reserved for the Master’s thesis, allowing you to pursue an in-depth research question or an industry-oriented project. Throughout semesters two and three you can tailor your studies by selecting modules that align with one or more specializations: (i) Data Analysis and Artificial Intelligence, (ii) Continuum Modelling and Simulation, (iii) Atomistic and Multiscale Simulation Methods, or (iv) Materials Science. You may also include one module from another faculty to broaden your profile. Graduates leave with practical simulation experience, transferable programming and ML skills, and the ability to critically assess modelling approaches—preparing them for research roles or technical positions in industry.
Curriculum requirements (summary)
For full details and updates, see the program page: https://tu-freiberg.de/en/master-computational-materials-science and the CMS student resource site: https://cms-tubaf.de/.
Who we’re looking for
The ideal candidate is curious about how materials and structures behave and enjoys using computers to explore those questions. You should be interested in mechanics of materials, numerical simulation methods, programming, and data analysis — especially in applying these tools to predict the performance of specimens, devices, or even hypothetical “virtual materials” with improved properties.
Students coming from applied computer science, applied mathematics or related backgrounds typically seek to gain domain knowledge that connects software development and machine‑learning/deep‑learning methods with materials science to broaden their professional skill set.
This program is interdisciplinary by design: it brings together aspects of engineering, physics, computer science and mathematics. Practical programming experience is helpful and will make the transition smoother, although it is not strictly required.
Minimum admission requirements
Winter Semester (International)
15 April 2026
Winter Semester (EU/EEA)
15 April 2026
Graduates are prepared for roles in industrial R&D, applied research groups and engineering teams where predictive simulation and data‑driven materials design are required. Typical tasks include developing and validating simulation models, implementing computational workflows, and applying machine learning to materials data to accelerate design and processing.
The programme also provides a solid foundation for doctoral studies in computational materials science, physics or engineering. Employers include high‑tech materials companies, automotive and aerospace industries, energy and manufacturing firms, and research institutes focused on multiscale modelling and materials informatics.
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