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Passing a medical licensing exam is one thing. Supporting a real clinical decision with incomplete data, rare edge cases, unstructured data, and a physician waiting for an answer is another problem entirely.
Our research group AI for Women's Health is hiring a student assistant to work on the VIOLET project, a nationally funded initiative developing a hybrid AI framework for guideline-based treatment decision support in gynecological oncology. The work combines NLP, knowledge graphs, retrieval-augmented generation (RAG), and LLM-based multi-agent systems and is grounded in real clinical data from the TUM University Hospital's data integration center.
Your job will be to help make these systems work reliably in practice: developing and evaluating NLP methods for extracting structured knowledge from clinical text, probing where foundation models fail to follow clinical reasoning, building evaluation pipelines that go beyond standard benchmarks, and developing methods to extract structured knowledge from clinical text.
What we're looking for:
- Bachelor's or Master's student in Computational Linguistics, NLP, Computer Science, Mathematics, Physics, or a related field
- Solid Python and PyTorch skills
- A strong interest in linguistic structure, information extraction, and the computational analysis of [clinical] language
- Rigor in how you think, code, and document
- Prior experience with clinical language or medical AI is welcome but not the deciding factor
What you get:
- Hands-on experience with challenging NLP problems that directly affect clinical decision-support systems
- Access to real, curated clinical datasets within a regulated research environment
- Substantial GPU infrastructure
- A short feedback loop between technical work and clinical application
- Both engineering and clinical mentorship within the team
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