Master Thesis on Graph-Based QA and Retrieval-Augmented Generation

About the role

Join Bosch Group as a Master thesis researcher to advance Graph‑Based Question Answering and Retrieval‑Augmented Generation (RAG) systems. You will explore how semantic web technologies can enhance the accuracy and faithfulness of AI‑driven answers using knowledge graphs.

Key responsibilities

• Conduct a comprehensive literature review on GraphRAG, knowledge‑graph QA, and semantic web methods.
• Investigate symbolic, ontological, and graph‑algorithm approaches to improve retrieval and generation pipelines.
• Design and implement a prototype that integrates RDF, OWL, SPARQL and ontology‑driven reasoning into a GraphRAG architecture.
• Validate the prototype on public benchmarks and Bosch‑internal datasets, comparing results with state‑of‑the‑art baselines.

Required profile

• Master studies in Computer Science, Machine Learning, Data Science, Cognitive Sciences or a related field with very good grades.
• Strong academic background in machine learning, graph data science, and/or semantic web technologies.
• Proactive attitude, ability to take ownership of open‑ended research problems, and motivated learning style.

Required skills

• Python programming with object‑oriented practices.
• Experience with RDF, SPARQL, OWL and related semantic‑web libraries.
• Familiarity with graph data‑science tools and concepts.
• Knowledge of machine‑learning techniques and RAG systems.

What we offer

• Hybrid work setup with 60% on‑site presence in Reutlingen.
• Opportunity to work on cutting‑edge AI research within a global technology leader.
• Access to Bosch internal datasets and mentorship from experienced researchers.