Student assistant (f/m/x) for analysing the Sim-to-Real gap in a mobile robot system - Embedded Syst

Vacancy-ID: 6785

Place of work: Ulm

Starting date: 01.02.2027

Career level: Student employment

Type of employment: Part time

Duration of contract: 6 months, with the option to extend

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund).

Hint: Eligibility for security clearance and the willingness to undergo a security check pursuant to §§ 8 ff. of the Security Clearance Act (SÜG) are conditions for employment at DLR.

At the DLR Institute for AI Security, we research and develop AI-related methods, processes, algorithms, technologies and execution and system environments. Our focus is on secure and standard-compliant AI, cybersecurity in open data and service ecosystems and AI, as well as automation in mobility and logistics.

What to expect

In the Execution Environments and Innovative Methods department, we bring together novel AI methods and challenging execution environments. In particular, we focus on the energy efficient implementation of AI for mobile systems, quantum AI and neuromorphic AI approaches. The department thus provides innovative AI approaches for use in safety-critical applications.

Your tasks

assist with the further development of a Mujoco reinforcement learning simulation environment using pyTorch and StableBaselinesX

build a Real-world use case setup with the mobile robot and objects for grasping similar to the simulation environment

integrate a camera for object detection and object position estimation

select example scenarios and run it on both – the simulation environment and the real-world experiment setup

Benchmark the result and compare the performance qualitative and quantitative

document the code and the analysis carried out, as well as the discrepancy between the simulation and reality, based on the results

Your profile

currently studying computer science or a related subject

good programming skills in Python and C++

good knowledge of Linux

a basic understanding of reinforcement learning

basic knowledge of PyTorch, ROS2, Mujoco and JAX

an interest in mobile, autonomous systems and their sensor technology

written and spoken German and English

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 6785) please contact:

Dr. Hans-Martin Rieser

Tel.: +49 731 400198 306

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