Master Thesis Deep Learning for Ultrasound

Your tasks

Automated parking in challenging scenarios requires a precise free space estimation. Ultrasound sensors are suitable for high‑precision predictions in close range. The topic of the thesis is the development of deep learning models for ultrasound, which exhibit good performance and are computational efficient.

• During your thesis you will have the possibility to develop and implement innovative ideas, new deep learning models for ultrasound data, especially as there is not a lot of research on deep learning for ultrasound sensors available yet.
• In addition, the ultrasound data can be fused with video data and processed together to enhance the performance.
• Furthermore, you will evaluate and compare the new models to existing baseline models.
• Working with real world data forms the foundation of your research to ensure the practicality and robustness of your solutions.

Your profile

• Education: Master studies in the field of Natural Sciences or Engineering like Machine Learning, Computer Science, Math, Statistics, Physics, Cybernetics, Electrical/Mechanical Engineering with very good grades
• Experience and Knowledge: strong knowledge of and practical experience in Deep Learning, Computer Vision, Machine Learning, and 3D perception systems; Python as well as very good knowledge of a deep learning framework (preferably PyTorch)
• Personality and Working Practice: you excel at working independently and are strongly intrinsic motivated
• Languages: fluent 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?
Kilian Rambach (Functional Department)
+49 173 490 2787

Apply now