We are looking for you to join our team in developing model- and learning-based simulation and control methods for robotic electric vehicles with X-by-wire architectures, in which the driver has no direct mechanical control over systems such as the steering. The high complexity associated with this technology, as well as the requirements for fault tolerance, are the driving forces behind the development of so-called surrogate models, which are intended for use in virtual training via reinforcement learning, among other applications. In your new role with us, you’ll work together with a dynamic, multidisciplinary team to bring your ideas to life. You’ll have the opportunity to develop and experimentally test the latest methods in system dynamics and control engineering—both virtually and on test beds—thereby making a decisive contribution to our mission

Your tasks

• analysis, development, and critical evaluation of scientific methods for nonlinear model-based and AI-based control methods
• development of machine learning algorithms, such as reinforcement learning and physically-based neural networks, for the control and simulation of mechatronic systems
• design of “AI-Aided Control Systems Engineering” toolchains based on technologies such as LLMs, Agentic-RAG, and MCP
• scientific analysis of methods for evaluating the robustness and reliability of model- and learning-based control methods
• experimental testing of the methods on near-production and experimental DLR test vehicles with by-wire control, and scientific evaluation of the tests using objective, numerical criteria, as well as assessment of these methods against the state of the art
• authoring scientific publications for international journals and conferences, as well as on the department blog

Your profile 

• a completed academic degree (Master’s or university diploma) in engineering in the fields of electrical engineering, computer science, robotics, or mechanical engineering with a focus on control engineering/information/automation technology, or other degree programs relevant to the position
• advanced expertise in control engineering and optimization techniques that goes beyond the content of introductory courses
• knowledge of at least one programming language: Modelica, MATLAB, C, or Python
• knowledge of machine learning methods (supervised and unsupervised learning)
• knowledge in the field of automotive engineering and electric mobility

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 6202) please contact:

Dr. Jonathan Brembeck 

Tel.: +49 8153 28 2472 

Apply now