We are seeking a highly motivated PhD candidate to join our team and conduct research in the field of cognitive radar for automotive applications. Typically, the hardware and software of automotive radar sensors are initially optimized during development, operating in fixed or ego-velocity dependent mode independent of the environment. However, in the cognitive radar approach, the radar waveform and signal processing are adapted based on previous sensing results and learned characteristics of the environment, resulting in improved sensing performance as well as potential cost savings. The goal of this PhD thesis is to research novel and innovative methods for cognitive Radar to enable adaptive sensing for automotive scenarios. As part of this research, you will work on the following key questions: How to learn characteristics of diverse automotive scenes? How to optimize radar sensing performance by adapting the waveform and signal processing (classical and AI-based)? Additionally, you will investigate methods for the release of cognitive radar functionality, ensuring safe and reliable operation with certain performance guarantees in all situations. During your PhD work, you will have the opportunity to build upon an existing comprehensive radar simulation and signal processing framework to develop a cognitive loop showcasing improved sensing as well as object detection performance for one or multiple perception tasks. Ideally, the cognitive loop will also be implemented in an existing HW demonstrator to verify the approach in real-world scenarios. Furthermore, you will have the chance to contribute to the scientific community through publications in top-tier radar as well as machine learning conferences and journals.