Master Thesis Multi-Teacher Distillation of Self-Supervised Models for 3D Perception

Your tasks

Self-supervised learning (SSL) and foundation models have advanced 3D perception for LiDAR and radar, but current models are trained independently, leading to fragmented representations.

• During your thesis you will develop a multi-teacher knowledge distillation framework to merge multiple pretrained models into a single, compact backbone, without labeled data.
• You will focus on task-agnostic distillation objectives to align heterogeneous feature spaces and prevent negative transfer, with extensions to integrate supervised teachers trained on unknown tasks. The resulting model will aim to deliver stronger, more general representations, reduced model capacity, and compatibility with existing perception stacks.
• Furthermore, you will conduct structured evaluations using both public datasets and internal real-world data, striving for excellent theoretical and practical results.

Your profile

• Education: Master studies in the field of Computer Science, Robotics, Engineering, Natural Sciences or comparable with excellent academic achievements
• Experience and Knowledge: experience with deep learning and corresponding frameworks; excellent programming skills (Python); knowledge of perception algorithms is a plus
• Personality and Working Practice: you excel at goal-oriented and structured work, are highly self-motivated, flexible, and apply logical thinking to maintain project oversight
• Languages: very good in English

Contact & additional information

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Mohamed Abdelsamad (Functional Department)
+49 173 3191588

Apply now