Thesis - Graph Neural Networks (GNN) for product related data (d/m/f)

Overview

Thesis - Graph Neural Networks (GNN) for product related data (d/m/f) role at Schaeffler .

Schaeffler is a dynamic global technology company. In the Sustainable Products & Advanced Materials (SP&AM) department, the team uses ML/AI tools to calculate product carbon footprints (PCFs) automatically and address data quality issues in fully automated workflows. GNNs are used for product related data that naturally unfolds in graph structures.

This Bachelor's/Master's thesis is available from April 1, 2026 for a duration of 6 months, with enrollment proof required at the time of the thesis. The position is intended for students (d/m/f) and is based in Regensburg, Germany when applicable.

Your Key Responsibilities

• Reviewing literature
• Creating a graph database ontology
• Collecting relevant product data
• Setting up a working prototype of a GNN

Your Qualifications

• Studies in a STEM field with a focus on computer science or a comparable field
• Very good communication skills in English (minimum B2)
• Proficiency in MS Office programs
• Familiarity with Python, graph databases, TensorFlow/Keras
• Good knowledge of machine learning
• Independent working style

As a global company, we value diversity and inclusion and encourage applications from all interested individuals. We strive to create a respectful, inclusive working environment.

Your Contact
Katarzyna Bogdanowicz

Seniority level

• Internship

Employment type

• Full-time

Job function

• Education and Training
• Industries: Manufacturing

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