Master's Thesis: Grounding LLM/VLM Behavior in Semantic Data Models (f/m/x)

Our team in the Applied Research department investigates and prototypes artifacts that enable more deterministic behavior of Large Language Models (LLMs) and Visual Language Models (VLMs). A robust, consistent AI-driven vehicle architecture depends on high-quality data and well-defined controlled vocabularies.

What awaits you?

• You will support the formulation of a valid research question aligned with an appropriate research methodology.
• Furthermore, you help to design and execute a study to answer the research question.
• In addition, you contribute to conducting a state-of-the-art review and analysis.
• Moreover, you will assist in exploring combinations between semantic data models and LLMs/VLMs.
• You will also support the design and execution of experiments with vehicle-related use cases.

What should you bring along?

• Studies in computer science, information technology, or a related field.
• Programming experience and previous experience with LLM-related projects.
• Familiarity with Visual Language Models (VLMs).
• Knowledge of semantic data models and controlled vocabularies.
• Familiarity with RDF-based Knowledge Graphs (KGs).
• High degree of self-motivation and strong organizational skills.
• Very good English language skills.

Would you like to explore innovative approaches to ground AI behavior in semantic data models and expand your knowledge? Then Apply now!

What do we offer?

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

Start date: earliest start 24.08.2026

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.