Research Associates (m/w/d) for Project “Storage as a Sustainability Enabler through Optimization, Machine Learning and High Performance Computing”

Research Associates (m/w/d) for Project “Storage as a Sustainability Enabler through Optimization, Machine Learning and High Performance Computing”

25.09.2026, Academic staff

The Institute of Advanced Study (IAS), together with the Professorship of Energy Management Technologies, is offering two research positions (doctoral candidate and postdoc) as part of a project titled "Energy Storage as an Enabler of Sustainability through Optimization, Machine Learning, and High-Performance Computing," supported by the Dieter Schwarz Foundation through a Courageous Research Grant. The project is led by IAS Fellow Prof. Anthony Papavasiliou.

Research will focus on the application of operations research in power system operations and electricity market analysis, with a specific focus on the funded proposal “Storage as a Sustainability Enabler through Optimization, Machine Learning and High Performance Computing”.

Description of the position:

The main activity of the researcher will be to conduct research under the supervision of Professor Anthony Papavasiliou (NTUA), recipient of the TUM-IAS Dieter Schwarz Courageous Research Grant 20206. Professor Christoph Goebel (TUM) will act as the host at TUM.

Possible topics of research include, but are not limited to:

• Exploiting GPUs for large-scale optimization
• Value stacking of storage in competitive wholesale electricity markets
• Optimal planning of electric power systems

For more information on the research group of Professor Papavasiliou, interested applicants are encouraged to consult the following link: https://ap-rg.eu/

The researcher will contribute to teaching activities at TUM. Positions are for one to three years and carry monthly salary with full benefits (according to TVL-13).

Required qualifications:

The ideal candidate should hold a degree in Electrical Engineering, Computer Science, Operations Research or a related field. The ideal candidate should have a solid background in optimization, computer science, mathematics, statistics, economics, or related disciplines, an interest for the application of operations research in energy systems and strong programming skills. Familiarity with parallel programming is highly desirable. Proficient knowledge of English is highly desirable. A background in power systems is desirable, though not required.