Master in AI4Energy

Rosenheim Technical University of Applied Sciences is a practice-oriented institution in Bavaria, offering a portfolio of applied bachelor and master programs across engineering, business, IT, design and health sciences. Founded as a modern applied university, TH Rosenheim serves around 7,400 students, including a significant international cohort, and emphasizes hands-on learning and industry relevance within a scenic Alpine region.

The university maintains strong partnerships with regional and international companies, enabling applied research projects, internships and dual-study pathways that integrate academic learning with workplace experience. English-taught programs such as Applied Artificial Intelligence and International Wood Technology reflect TH Rosenheim's commitment to globalised curricula, while small class sizes and up-to-date facilities support practical skill development.

Students benefit from targeted support for international applicants, orientation activities and career services that facilitate professional entry in Germany and beyond. The surrounding region offers a high quality of life and excellent opportunities for outdoor recreation, complementing an educational model focused on employability. For prospective students seeking applied, industry-connected education in Germany, TH Rosenheim combines technical depth with practical experience and strong student support.

Students in the AI4Energy programme will engage with the BuilDa laboratory, which generates building data for data-driven building energy and control research, focusing on simulation and data frameworks that replicate thermal building dynamics and support machine learning methods.

This Master's degree programme places a strong emphasis on the development of AI-based models for energy systems and applications.

Students explore various methodologies, including Reinforcement Learning, Model Predictive Control, and deep learning approaches, such as neural networks, to optimise building energy systems and enhance occupant comfort.

Expertise in machine learning, statistical analysis, and control strategies are developed through learning from and collaboration with renowned professors such as Prof. Dr. Benjamin Tischler and Dr. Fabian Raisch.

The inclusion of research projects, such as DamoTL, offers hands-on experience in real-world energy optimisation, fostering a deep understanding of the subject matter.

The curriculum aligns with research activities, ensuring that students are well-prepared with practical skills and theoretical knowledge, and are engaged in cutting-edge research in energy systems and building automation.