Master Thesis Representation Learning for Radar 3D Perception
Robert-Bosch-Campus 1, 71272 Renningen, Germany
Full-time
Robert Bosch GmbH
Company Description
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!
Job Description
* As part of your Master thesis, you will explore and evaluate representation learning techniques using types of radar data, including point clouds, spectrum data, and more.
* You will conduct experiments and perform data analysis using both publicly available radar datasets and Bosch's proprietary datasets.
* Last but not least, you will investigate multi-frame radar data fusion from multiple measurement cycles and explore how it can enhance radar perception through advanced learning methods.
Qualifications
* Education: Master studies in the field of Computer Science, Electrical Engineering, Robotics or comparable with strong academic performance
* Experience and Knowledge: in implementing machine learning / deep learning applications, Python (preferable PyTorch); knowledge of radar signal processing is an advantage
* Personality and Working Practice: you are an open-minded and reliable individual who is eager to learn
* Languages: fluent in English, German is a plus
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?
Sherif Abdulatif (Functional Department)
+49 711 811 14364
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