Thesis - Optimization of Graph Neural Networks for Product related data (d/f/m)
Indexed description
General Information
One of the main tasks of the department "Sustainable Products & Advanced Materials" (SP&AM) is the assessment of product carbon footprints (PCF) of the Schaeffler portfolio. In order to calculate PCFs automatically the digitalization team (CESD) of this department collects, stores and processes product related data. Addressing data quality issues is a special challenge within fully automatized workflows. Today there are powerfull ML/AI tools available in order to tackle this issue. One of these tools are Graph Neural Networks (GNNs), which are of peculiar interest when it comes to product related data that naturally unfolds in graph structures.
A basic requirement for a thesis at Schaeffler is proof of enrollment at the time of the thesis. This position is available from October 2026 for a duration of 6 months.
Your Key Responsibilities
- Literature review
- Improvement of working prototype of GNN for product related data
- Studies in the field of MINT with focus on computer science or a comparable field of study
- Proficient in MS Office programs, Python, (Graph)Databases, PyTorch/PyG, Machine learning methods & Optimization methods
- Good written and spoken English skills (B1)
- Excellent communication skills
- Quick comprehension, proactive, creative and eager to learn
We place great importance on creating a working environment that promotes and actively supports diversity and inclusion. We look forward to receiving applications from all interested people, because your potential is what counts for us!
www.schaeffler.com/careers
Your Contact
Vitesco Technologies GmbH
Sylwia Kocenda
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