Thesis on AI-Driven Map Matching and Path Prediction on Semantically Enriched Road Networks

Map matching and path prediction are core capabilities for autonomous driving. Our team at the BMW Group explores data-driven approaches that combine symbolic reasoning and machine learning, operating on real-world map data to improve robustness, accuracy, and interpretability in complex urban environments.

What awaits you?

• You will support modeling road connectivity and constraints in RDF and implementing rule sets to compute the most probable path using a rule-based reasoner.
• Furthermore, you help build features from observations and the road graph, learning graph embeddings and training models to predict the next link or path.
• In addition, you contribute to formulating path prediction as a reinforcement learning problem, integrating graph embeddings and training agents such as DQN or actor-critic.
• Moreover, you will assist in developing sequence-to-sequence or transformer-based models to align GPS trajectories to graph-aligned edge sequences and comparing them to baselines.
• Darüber hinaus wirkst du mit beim Experimentieren mit graph-aware attention or constrained decoding to inject RDF structure and semantics into learning models.
• Additionally, you support designing metrics and scenarios, measuring accuracy, robustness, efficiency, and interpretability, and running ablations on embeddings and semantic attributes.

What should you bring along?

• Studies in computer science, data science, electrical engineering or a related field.
• Solid background in machine learning, including supervised learning and basic reinforcement learning.
• Experience with Python and machine learning or deep learning frameworks such as PyTorch or TensorFlow, plus data processing libraries.
• Familiarity with graph representations and knowledge graphs such as RDF and SPARQL; comfort with graph or knowledge graph embeddings is a plus.
• Understanding of graph neural networks or representation learning methods.
• Software engineering skills for reproducible experiments, including version control, clean code, and benchmarking.
• Good German and English skills.

You are enthused by new technologies and an innovative environment? Then Apply now!

What do we offer?

• Comprehensive mentoring & onboarding.
• Personal & professional development.
• Flexible working hours.
• Mobile work.
• Attractive & fair compensation.
• Student apartments (subject to availability & only at the Munich location).
• And much more, see bmw.jobs/whatweoffer

Start date: earliest start 05/15/2026

Duration: 6 months

Working hours: Full-time

You can find helpful tips on your application and the application process here .

At the BMW Group, we place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants. Learn more here .