Student for Master's Thesis Transfer Learning with 3D Design Data

Life is always about becoming... In life it is about going on a journey to become the best version of our future self. As we discover new things, we face challenges, master them and grow beyond us.

Apply to Mercedes-Benz and find the area where you can develop your talents individually. You will be supported by visionary colleagues who share your pioneering spirit. Joining us means becoming part of a global team whose goal is to build the most desirable cars in the world. Together for excellence.

Number: MER0003TL9 Mercedes-Benz is at the forefront of the automotive industry and actively shapes the future of mobility. In the research and development department (R&D) of Mercedes-Benz Cars, we work on the next generation of vehicles and drive innovations in every field of vehicle development. By using large amounts of data and advanced digital methods and AI models for CAx, we accelerate the design and validation cycles for vehicle components.

Industrial AI applications are often limited by the lack of labelled data that are specially tailored to specific tasks. This limits both the performance and the generalisation of the models. In contrast, a wealth of information is created in the life cycle of CAD data, which, however, are largely unwritten or only relevant to other but related tasks. The potential of this previously unused resource is crucial for the progress of AI technologies in the industrial environment. Techniques such as transfer learning and domain adaptation have proven to be effective approaches to bridge the gap between labeled and unlabeled or cross-domain data. These methods enable more effective knowledge transfer and representation learning, in particular in challenging scenarios such as generative 3D tasks, in which annotated data sets are particularly limited for different tasks.

Possible tasks: Working on the development of deep learning models for 3D objects

Development of advanced 3D transfer learning methods

Analysis of pre- and post-processing techniques for 3D geometries

Collection and processing of data for the internal dataset

The activity can begin from December 2025.

Study programme in the field of computer science, software technology or a related field

Secure knowledge of German and English in word and writing

Embossed programming skills (e.g. in Python)

Experience with deep learning frameworks (e.g. PyTorch, TensorFlow) and related projects

Knowledge in the field of 3D computer vision

Teamability and independent working

Additional information: We are looking forward to your online application with CV, lettering, certificates, current enrollment certificate with an indication of the semester and proof of the regular study period. Please do not forget to mark your documents as ''relevant for this application' in the online form and to observe the maximum file size of 5 MB. Further information on the setting criteria can be found here. Disabled and equalized applicants are welcome! The severely disabled representative (SBV-Sindelfingen@mercedes-benz.com) is happy to support you in the application process. HR Services will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

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