Research Associate and Doctoral Candidate (f/m/d) for Research Project on the Optimal Use of Load Flexibility in Industry

17.07.2026,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 use of load flexibility in industry.

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 co-optimization of large-scale battery storage systems and industrial flexibility. The consortium comprises various industrial companies and service providers. Within the scope of the project, TUM’s role is to research new optimization and control methods for utilizing storage and load flexibility at industrial sites in a cost- and emission-optimized manner. This work will involve employing new methods that combine model-based mathematical optimization with machine learning.

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 an engineering-related field (in particular Electrical Engineering and Computer Science)
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 August 3, 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.

Kontakt: applications.emt@ed.tum.de