Summary
The Technical University of Munich (TUM) offers a fully funded PhD position in the Research Group AI-Assisted Healthcare at TUM University Hospital. This position focuses on developing advanced AI methods to support multidisciplinary tumor boards in oncology through the integration of large language models and temporal disease modeling.
PhD Position in Medical AI for Clinical Language Models, University of Munich, Germany
Designation
PhD Candidate (m/f/d) – Medical AI for Clinical Language Models and Temporal Disease Modeling in Oncology
Table
| Details | Information |
|---|---|
| Research Area | AI-Assisted Healthcare, Oncology, Machine Learning |
| Location | TUM University Hospital, Klinikum rechts der Isar, Munich, Germany |
| Eligibility/Qualification | Completed Master’s degree in Computer Science, Data Science, Computational Linguistics, Mathematics, or a related field; Strong programming skills in Python; Experience with modern deep learning frameworks (e.g., PyTorch); Background in machine learning, especially in NLP, large language models, or sequence modeling. |
| Job Description | – Design and implement LLM-based pipelines – Develop semantic harmonization strategies – Build transformer-based temporal models – Contribute to simulation-based evaluations – Publish results and present at conferences |
| How to Apply | Send a single PDF application including a cover letter, CV, and transcripts to s.ziegelmayer@tum.de |
| Last Date to Apply | Applications will be reviewed on a rolling basis until the position is filled. |
Research Area
The research focuses on developing transparent, agent-based AI methods for oncology, utilizing large multimodal datasets and collaborating closely with clinical partners to assist in decision-making processes during tumor board meetings.
Location
TUM University Hospital, Klinikum rechts der Isar, Munich, Germany
Eligibility/Qualification
- Completed Master’s degree (or equivalent) in Computer Science, Data Science, Computational Linguistics, Mathematics, or a related field.
- Strong programming skills in Python and experience with modern deep learning frameworks (e.g., PyTorch).
- Solid background in machine learning, preferably with experience in NLP, large language models, or sequence modeling.
- Interest in clinical and biomedical applications; prior exposure to medical data is welcome but not required.
- Very good command of English; German is an asset but not necessary.
- Independent, rigorous, and collaborative working style.
Job Description
- Design and implement LLM-based pipelines for extracting oncological events from clinical free text.
- Develop semantic harmonization strategies using standardized vocabularies (e.g., SNOMED CT, ICD-10, LOINC).
- Build transformer-based temporal models representing patient trajectories.
- Develop interpretability methods linking model outputs to source documents.
- Contribute to simulation-based evaluations together with clinical partners.
- Publish results and present findings at international conferences.
How to Apply
Interested candidates should send their application as a single PDF, including a cover letter, CV, and transcripts, to: s.ziegelmayer@tum.de.
Last Date to Apply
Applications will be reviewed on a rolling basis until the position is filled.
For more information, visit TUM AI-Assisted Healthcare or the Technical University of Munich website.
