Karlsruhe Institute of Technology 
Eggenstein-Leopoldshafen, Germany Angebotszusammenfassung
The Karlsruhe Institute of Technology in Germany invites applications for a full‑time Academic Researcher (f/m/d) in the Institute for Automation and Applied Informatics. The role focuses on developing and testing reinforcement‑learning‑based solutions for coordinating flexible energy assets within virtual power plants and smart grids. Responsibilities include literature review, model design, simulation, hardware implementation, experimentation, data analysis, and publication. Candidates should hold a master’s degree in computer science, engineering, mathematics, or a related field, with strong expertise in machine learning, statistics, energy informatics, electrical engineering, and stochastic processes, plus solid programming skills. The position offers a competitive salary of €59,300–€66,800 gross per year and the possibility to pursue a Ph.D. and teaching duties.
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Projektbeschreibung
Your tasks
A major challenge of the energy transition is managing variable electricity generation from renewable energy sources and coordinating it with storage systems and flexible consumers. The bundling of flexible or controllable facilities within virtual power plants, intelligent districts, or as intelligent areas is a central approach to reducing system complexity. The goal of this position is the conception, implementation, evaluation, and testing of new solutions for coordinating flexible facilities. The focus lies on the application of reinforcement learning and the consideration of stochastic influences Specifically, you will have the following responsibilities:
- Researching and familiarizing yourself with the topics of smart grids and virtual power plants, as well as the current state of the art, and identifying research gaps
- Designing models and algorithms, as well as programming
- Implementing the solutions in computer simulations and subsequently using the Energy Lab’s actual hardware and the Campus North grid
- Conducting experiments, analyzing the results, and deriving new insights
- Publishing the results in academic journals and at conferences
- Assistance with teaching
In addition to research activities, there is the opportunity to pursue a Ph.D.
Your profile
- A completed master’s degree in computer science, mechanical engineering, mathematics, Industrial Engineering and Management or a comparable field of study
- Excellent knowledge in the fields of Machine Learning and statistics
- Solid expertise in the areas of energy informatics and electrical engineering
- Strong understanding of stochastic processes/probability theory
- Programming experience
- Excellent written and spoken English skills
- Strong teamwork and communication skills, a strong willingness to learn, and a solution-oriented approach to work
