For more than one decade the Institute of Measurement and Control Systems (MRT) at KIT has been one of Germany's leading autonomous driving labs. Its unique advantage is the availability of experimental vehicles with sensors, actuators and a complete software stack for automated urban driving. The existing equipment and experience not only enables continuous cutting-edge research in all areas of autonomous driving, but also a wide range of collaborations with leading industrial partners. Research highlights of the MRT are, among others, the successful participation in autonomous driving competitions as well as the development of highly influential benchmark suites like KITTI and KITScenes.
Large foundation models and vision-language-action models increasingly rely on long context windows to reason over high-resolution images, videos, multi-camera sensor streams, maps, interaction histories and language instructions. However, standard attention mechanisms scale unfavorably with context length, and large models remain difficult to deploy under real-time, memory and energy constraints in autonomous systems. You will investigate efficient long-context models for computer vision and VLA systems and apply it to autonomous driving.
The research will combine attention optimization, token reduction, model pruning and quantization to make large models scalable while preserving accuracy, robustness, and downstream action quality. The research will build on established ideas from Transformers, long-sequence attention, memory-efficient attention, vision transformers, deep compression, and recent VLA systems.
You are a dedicated and creative person with
Job location Karlsruhe (and Eggenstein-Leopoldshafen)
Salary Salary category 13 TV-L; classification is based on personal and professional qualifications.
Contract duration 3 years
Contact person in line-management Dr. Martin Lauer martin.lauer@kit.edu
If you have general questions about the application process, please contact Julia Boldt Personalservice (PSE) julia.boldt@partner.kit.edu
At KIT we value the diversity of our employees; different perspectives and backgrounds enrich our work. We therefore welcome applications from all candidates. Women are especially encouraged to apply. Applications from recognized severely disabled individuals are given preferential consideration when qualifications are equal.
Application up to: 2026-11-08 Job posting number: 1252/2026
Tagged as: Life Sciences
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