The Professorship of Intelligent Space and Energy Systems focuses on the computational solution of sequential decision-making problems for (safety-critical) real-world systems. In addition, we contribute to the advancement of these methods by investigating knowledge transfer in sequential decision-making and by addressing safety aspects associated with the deployment of policies trained through deep reinforcement learning.

The primary application domain is space systems. In this context, our research addresses the optimized control and testing of propulsion systems—for example through the dynamic tuning of controllers—as well as automated planning and scheduling for spacecraft. Insights derived from these activities are further transferred to the energy sector and related fields, where analogous challenges in control, planning, and reliability arise.

If you are interested in one of these areas, you are very welcome to attend one or more of our courses or write your Bachelor and/or Master thesis in our group.