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

Robert Bosch Group; Renningen; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

A leading technology company in Renningen is seeking a Master’s student for a thesis on Multi-Teacher Distillation of Self-Supervised Models for 3D Perception. The role involves developing a framework for merging models without labeled data and requires strong skills in deep learning and Python. Candidates must be enrolled in relevant Master studies and have a good command of English. The position is full-time for 6 months with flexible starting dates.

Qualifikationen

• Experience with deep learning and corresponding frameworks.
• Excellent programming skills in Python.
• Very good command of English.

Aufgaben

• Develop a multi-teacher knowledge distillation framework.
• Focus on task-agnostic distillation objectives.
• Conduct structured evaluations using public and internal datasets.

Kenntnisse

Deep learning experience

Python programming

Knowledge of perception algorithms

Goal-oriented work

Logical thinking

Ausbildung

Master studies in Computer Science, Robotics, Engineering, or Natural Sciences

Jobbeschreibung

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

Full-time

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Overview

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.

Responsibilities

• 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.

Qualifications

• 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.

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)
[phone]