Engineer (f/m/x) - for AI-based system dynamics and control engineering

German Aerospace Center (DLR); Oberpfaffenhofen; On-site; EUR 65,000 - 90,000; Full time

Job summary

The German Aerospace Center (DLR) Institute of Vehicle Concepts (FK) in Oberpfaffenhofen is developing model- and learning-based control methods for robotic electric vehicles with by-wire architectures. You will work in a dynamic, multidisciplinary team to advance surrogate models and validate them on test beds, contributing to climate-friendly mobility.

We seek candidates with a Master’s in engineering, strong control engineering and optimization expertise, and proficiency in Modelica, MATLAB,

Qualifications

• Master’s or Diplom in engineering with focus on control engineering
• Advanced expertise in control engineering and optimization
• Programming in Modelica, MATLAB, C, or Python
• Knowledge of machine learning methods
• Knowledge in automotive engineering and electric mobility

Responsibilities

• Analyze, develop, and evaluate nonlinear model-based and AI-based control methods
• Develop ML algorithms (reinforcement learning, neural nets) for control and simulation
• Design AI-Aided Control Systems toolchains using LLMs, Agentic-RAG, MCP
• Assess robustness and reliability of model- and learning-based control methods
• Experimentally test methods on DLR test vehicles with by-wire control
• Author scientific publications for journals, conferences, and the department blog

Skills

Control engineering

Optimization

Machine learning

Modelica

MATLAB

Python

C

Automotive engineering

Electric mobility

Education

Master’s degree in engineering

Tools

Modelica

MATLAB

Python

C

Job description

The Institute of Vehicle Concepts (FK) of the German Aerospace Centre (DLR) is internationally recognised for the design of future road and rail vehicles that enable climate and environmentally friendly mobility while being affordable and user-friendly at the same time. We research and demonstrate the required key technologies and maintain close cooperation with other scientific institutions as well as industrial and political bodies.

What To Expect

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 look forward to getting to know you!

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

Dr. Jonathan Brembeck

Tel.: [phone]