Sindelfingen: Student for master’s thesis – Transfer Learning on 3D Construction Data

Mercedes-Benz is at the forefront of shaping the future of the automotive industry. At Mercedes-Benz Cars Research and Development (R&D), we are driving innovation to develop the next generation of automobiles. Our commitment to innovation extends to every aspect of the car design process. By leveraging vast amounts of data and advanced digital methods and AI models for CAx, we accelerate the construction and validation cycles for car part designs.

Industrial AI applications are frequently constrained by the scarcity of labeled data tailored to specific tasks, which limits the performance and generalizability of models. In contrast, the CAD lifecycle generates a wealth of data that remains largely unlabeled or relevant to different but related tasks. Unlocking the potential of this underutilized resource is critical for advancing AI capabilities in industrial settings. Techniques such as transfer learning and domain adaptation have emerged as powerful approaches to bridge the gap between labeled and unlabeled or cross-domain data. These methods enable more effective knowledge transfer and representation learning, particularly in challenging scenarios like 3D generative tasks, where annotated datasets are especially limited for different tasks.

Possible tasks include:

• Collaborating on the development of deep learning models for 3D objects

• Developing the advanced transfer learning method in 3D domain

• Investigating pre- and post-processing techniques for 3D geometry

• Collecting and processing data for the internal dataset

The activity can start in december 2025.

• Studies in the field of computer science, software engineering, or a related field of study

• Proficiency in both written and spoken German and English

• Strong programming skills (e.g., Python)

• Experience of deep learning frameworks (e.g., PyTorch, Tensorflow) and related projects

• Knowledge of 3D Computer Vision

• Strong teamwork and collaboration skills

Additional Information:

We look forward to receiving your online application, including a resume, cover letter, certificates, current certificate of enrollment stating your semester, and proof of the standard period of study. Please remember to mark your documents as "relevant for this application" in the online form and observe the maximum file size of 5 MB.

You can find further information on the hiring criteria here.

Severely disabled applicants and applicants with equivalent status are welcome! The representative for severely disabled employees (SBV-Sindelfingen@mercedes-benz.com) will gladly support you in the application process.

HR Services will be happy to help you with any questions you may have about the application process. You can reach us by email at myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mon-Fri 10am-12pm & 1pm-3pm).