PhD Position (m/f/d) - Medical AI for Clinical Language Models and Temporal Disease Modeling in Oncology

PhD Position (m/f/d) - Medical AI for Clinical Language Models and Temporal Disease Modeling in Oncology

03.06.2026, Academic staff

The Research Group AI-Assisted Healthcare at TUM University Hospital is offering a fully funded PhD position (TV-L E13, 36 months) in a BMFTR-funded consortium on agent-based AI for multidisciplinary tumor boards. The successful candidate will develop LLM-based pipelines, semantic harmonization, and transformer-based temporal models on a large multimodal oncology dataset.

About the position

The Research Group AI-Assisted Healthcare at TUM University Hospital (Klinikum rechts der Isar) is seeking a highly motivated PhD candidate to join a BMFTR-funded collaborative research project on transparent, agent-based AI methods to support multidisciplinary tumor boards in oncology.

You will develop methods to transform heterogeneous oncology documentation into structured, time-aligned patient trajectories, combining large language models, semantic harmonization, and transformer-based temporal modeling. You will also contribute to the simulation-based evaluation of the resulting decision-support outputs in realistic tumor board scenarios. The work is carried out in close collaboration with partners at TU Munich and RWTH Aachen.

This is a genuinely interdisciplinary role at the interface of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment.

Your tasks

• Design and implement LLM-based pipelines for extracting oncological events (diagnoses, staging, treatments, disease course) from clinical free text
• Develop semantic harmonization strategies using standardized vocabularies (SNOMED CT, ICD-10, LOINC) and cross-institutional mapping
• Build transformer-based temporal models that represent patient trajectories and treatment response
• Develop interpretability and attribution methods linking model outputs to their source documents
• Contribute to the design and analysis of simulation-based reader evaluations together with clinical partners
• Publish results at leading venues and present at international conferences

Your profile

• 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, ideally 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 a requirement
• Independent, rigorous, and collaborative working style

Our offer

• A PhD position in a well-funded, nationally networked research consortium with strong clinical and methodological partners
• Access to large-scale multimodal oncology datasets and substantial computing infrastructure
• Structured doctoral training and supervision within the TUM environment
• Full-time salary according to TV-L E13, in line with standard public-sector conditions
• A diverse, interdisciplinary, and supportive team
• The position is initially limited to 36 months in line with the project duration

Contact and application

Please send your application as a single PDF, including a cover letter, CV and transcripts to: [email protected]

Applications will be reviewed on a rolling basis until the position is filled. The selection process is organized in two stages.

The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

Data Protection Information:
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Kontakt: [email protected]

More Information

https://radiologie.mri.tum.de/en/ai-assisted-healthcare