电池储能优化运行研究助理/博士生

Back to News Board Browse in News Research Associate and Doctoral Candidate (f/m/d) for research project on economically optimal and grid-friendly operation of battery energy storage 24.08.2026, Academic staff The Professorship of Energy Management Technologies at TUM’s School of Engineering and Design is looking for a Research Associate and Doctoral Candidate (f/m/d) for a research project on the optimal energy storage control under distribution system constraints. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like to work in a vibrant and international research environment? Then let’s design the energy management systems of the future together! Our research focus: The researchers working at the Professorship of Energy Management Technologies are focusing on the design and evaluation of innovative data-driven and Machine Learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop novel optimization methods, Machine Learning algorithms, and prototypical Energy Management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable future. These EMS coordinate distributed renewable generation like solar and wind, flexible loads like heat pumps and electric vehicles, and distributed energy storage like stationary batteries and hydrogen storage to maximize energy efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes the evaluation of existing systems, extensive simulation-based analyses, as well as the implementation and validation of algorithm and system designs in real world settings. Your tasks: You will be working on a research project—funded by the Federal Ministry for Economic Affairs and Energy—focused on the optimal energy storage control under distribution system constraints. The consortium comprises various companies, cooperatives and research organizations. TUM’s role in the project is to research new optimization and control methods for operating large-scale battery storage systems in a way that is both economically optimal within the electricity market and beneficial to the grid, specifically by automatically adapting to the local capacity of the respective distribution network. The approach involves employing new methods that combine model-based mathematical optimization with machine learning. Realistic simulation of battery storage systems and distribution networks accounting for varying levels of available information plays a central role in this process. The Professorship of Energy Management Technologies closely collaborates with other professorships at TUM, industry partners, and partner research institutions. You will support us in making these cooperations efficient and productive. As Research Associate you will also support our teaching activities in several Bachelor and Master programs offered by the School of Engineering and Design and the School of Computation, Information and Technology. You will help us to prepare teaching material, serve as teaching assistant in our lectures, support lab courses, and supervise student research. Your profile: Above-average master’s degree in Electrical Engineering Hands-on mentality with practical experience in optimization and control of energy systems Strong interest in energy technology and systems Good software engineering skills First experiences with the application of Machine Learning methods Inquisitive and passionate about research and knowledge transfer Independent, creative, and committed way of working Ability to think conceptually and analytically Very good command of English Good command of German Our offer: We offer you the opportunity to do research within a team of highly motivated researchers and benefit from the research environment offered by one of the best universities in Europe and worldwide. We support your doctoral dissertation in the research area outlined above. The offered position (pay grade TV-L 13) is initially limited to 2 years. Further employment is possible and intended. Your application: We are looking forward to your application until September 23, 2026. Please submit it as one single PDF file via email to applications.emt@ed.tum.de. Your application should contain the following documents: Curriculum vitae Complete academic transcripts Letters of reference from previous positions held, including internships Bachelor and Master thesis The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM. Kontakt: applications.emt@ed.tum.de Print

Your profile: Above-average master’s degree in Electrical Engineering Hands-on mentality with practical experience in optimization and control of energy systems Strong interest in energy technology and systems Good software engineering skills First experiences with the application of Machine Learning methods Inquisitive and passionate about research and knowledge transfer Independent, creative, and committed way of working Ability to think conceptually and analytically Very good command of English Good command of German