Summary
The Professorship of Energy Management Technologies at TUM’s School of Engineering and Design is seeking a highly motivated Research Associate and Doctoral Candidate to work on a research project focused on the economically optimal and grid-friendly operation of battery energy storage systems. This role involves developing cutting-edge optimization and control methods, combining model-based mathematical optimization with machine learning, within a vibrant and international research environment.
Research Associate and Doctoral Candidate (f/m/d) – Optimal Energy Storage Control
Designation
Research Associate and Doctoral Candidate (f/m/d)
Research Area
- Optimal energy storage control under distribution system constraints
- Data-driven and Machine Learning-based systems for renewable energy integration and energy efficiency
- Novel optimization methods and Machine Learning algorithms for Energy Management Systems (EMS)
- Simulation and validation of algorithm and system designs in real-world settings
Location
TUM’s School of Engineering and Design, Technical University of Munich (TUM)
Eligibility/Qualification
- 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
Job Description
As a Research Associate and Doctoral Candidate, you will:
- Work on a research project funded by the Federal Ministry for Economic Affairs and Energy, focusing on optimal energy storage control under distribution system constraints.
- Research new optimization and control methods for operating large-scale battery storage systems, aiming for economic optimality in the electricity market and grid benefits, by automatically adapting to local distribution network capacity.
- Employ new methods combining model-based mathematical optimization with machine learning.
- Conduct realistic simulations of battery storage systems and distribution networks, accounting for varying levels of available information.
- Collaborate with other professorships at TUM, industry partners, and partner research institutions.
- Support teaching activities in Bachelor and Master programs, including preparing teaching material, serving as a teaching assistant, supporting lab courses, and supervising student research.
- Pursue a doctoral dissertation in the outlined research area.
How to Apply
Please submit your application 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
Last Date for Apply
September 23, 2026
Apply Link
Email your application to: applications.emt@ed.tum.de
