Master Thesis in Sensor Fusion for End-to-End Autonomous Driving Systems

Job Description

Are you ready to work with cutting-edge deep learning based end-to-end approaches that learn driving behavior directly from sensor data? While these models scale effectively, their robustness in rare and safety critical scenarios remains a key challenge, especially when integrating new sensor modalities. With this thesis, you will have the opportunity to improve performance and reliability by combining prior knowledge with novel sensor inputs.

• You will explore how new sensor information can be integrated into end-to-end architectures and vision-language-action (VLA) models using explicit text-based or latent representations.
• As part of the thesis, you will simulate complex scenarios to train and evaluate the driving models.
• In addition, you will conduct in-depth evaluation and comparison of the developed approaches.

Qualifications

• Education: master studies in the field of Computer Science, Robotics, Applied Mathematics or comparable, with good grades
• Experience and Knowledge: strong theoretical understanding of deep learning principles combined with hands-on implementation; proficiency in Python and familiarity with deep learning frameworks such as PyTorch
• Personality and Working Practice: you bring an open-minded and innovative perspective, communicate clearly, work independently, and approach new topics with curiosity
• Work Routine: your on-site presence is required
• Languages: fluent in English

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?
Christian Löwens (Functional Department)
+49 5121 49 7176

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