TUM-LSY Open Postdoctoral Position (m/w/d)

TUM-LSY Open Postdoctoral Position (m/w/d)

22.12., Academic staff

We are an interdisciplinary team at the Chair of Safety, Performance and Reliability for Learning Systems, and we are looking for exceptional postdoctoral researchers to join our team.

The postdoctoral positions will be full-time (%, TV-L E13). We are committed to fostering a diverse and inclusive research environment, and we strongly encourage candidates from underrepresented groups to apply. The review process is on a rolling basis. Please refer to the “Application Procedure” section for details on how to apply.

Current Openings

Safety, Performance and Reliability in Robot Learning (ID: TUEILSY-POSTDOC-RL)

Robots that learn from data promise greater autonomy and performance, but their deployment in the real world hinges on the ability to guarantee safety, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise, and unmodeled dynamics remains a key challenge. This position focuses on developing and validating methods that jointly address safety, performance, and reliability of learning-based control and decision-making algorithms on real robotic systems operating in unstructured and dynamic environments.

This work is connected to the Robotics Institute Germany (RIG) and relates to the thematic cluster Safety, Reliability and Resilience of AI-based Robotics. The successful candidate will have the opportunity to engage with the RIG network, collaborate with academic and industrial partners, and contribute to joint research activities, publications, and surveys.

Requirements

• PhD degree (or near completion) in robotics, control, machine learning, or a related field;
• Strong publication record demonstrating independent and creative research;
• Solid programming experience in C++ and/or Python, and familiarity with modern robot learning frameworks;
• Proven expertise in one or more of the following areas: robot learning, safety-critical control, probabilistic modeling, verification, or optimization;
• Excellent communication skills in English and the ability to work both independently and collaboratively in an interdisciplinary team;
• Prior experience with real robotic systems and deployment of learning-based controllers.

If you do not satisfy all requirements but are very interested in the position, please feel free to apply and/or reach out to us for questions.

Application Procedure

Please submit your application package via the following link: Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.