The Professorship of Intelligent Space and Energy Systems focuses on the application of state-of-the-art artificial intelligence methods for sequential decision-making under uncertainty. In particular, we investigate the safe, ongoing acquisition and adaptation of new skills through deep reinforcement learning. A further focus lies on ensuring that policies learned in this manner can be deployed and reused in a reliable way, especially in safety-critical systems.

The primary application domain is space systems. In this context, our research addresses the optimized control and testing of propulsion systems 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.