Karlsruhe Institute of Technology 
Karlsruhe, Germany Angebotszusammenfassung
The Karlsruhe Institute of Technology (KIT) invites applications for a full-time PhD position within the Institute of Measurement and Control Systems. This role focuses on developing efficient long-context large models for vision and vision-language-action systems, specifically applied to autonomous driving research. Candidates will investigate attention optimization, token reduction, model pruning, and quantization techniques to enhance scalability while maintaining accuracy under real-time constraints. The ideal applicant holds an excellent Master’s degree in engineering, computer science, or robotics, with proven expertise in deep learning architectures and software development. Responsibilities include independent scientific work and collaboration within an international team at Germany’s leading autonomous driving lab. The position offers an annual gross salary ranging from 59,300 to 63,600 EUR. Applicants should demonstrate creativity, dedication, and eagerness to contribute to cutting-edge research involving experimental vehicles and industrial partnerships.
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Projektbeschreibung
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.
Your tasks
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.
Your profile
You are a dedicated and creative person with
- an excellent Master’s degree in engineering, computer science, robotics or similar
- proven knowledge of deep learning and large model architectures
- experience in software development for deep learning
- interest in working independently and scientifically, and
- eagerness to actively participate in a great international team.
