Technical University of Munich
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2 weeks ago
PhD in Computer Science in Trustworthy AI for Gynecological Precision Oncology Technical University of Munich in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position (E13 TV-L, full-time, 36 months). Funding includes support for open-access publications, international conferences and research mobility grants, plus TUM Graduate School support and employee benefits.
Country
Germany
University
Technical University of Munich
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About this position
Technical University of Munich (TUM) is advertising a fully funded PhD position in Computer Science at the Institute for AI and Informatics in Medicine (AIIM), within the SEQUORA project. The project focuses on trustworthy, clinically useful AI for women with rare and advanced gynecological cancers.
The research combines longitudinal real-world clinical data, causal inference, agent-based AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and knowledge-based methods. The goal is to support therapy-sequence decisions, clinical-trial matching, and evidence synthesis in real-world tumorboard workflows. The work is interdisciplinary and involves medical informaticians, gynecologic oncologists, and consortium partners.
Key tasks include co-designing and implementing clinical-informatics components, harmonizing longitudinal data models for more than 3,500 treatment courses, aligning data with the OMOP Common Data Model, developing and evaluating AI functions, translating clinical questions and trial criteria into machine-processable representations, and implementing traceability, uncertainty communication, and human-oversight mechanisms. The position also expects publication output and contributions to open-source research artifacts.
Eligibility highlights include an excellent Master's degree in Computer Science, Medical Informatics, Health Informatics, Data Science, or a closely related field; strong research record; experience with machine learning and/or NLP; strong Python skills; familiarity with PyTorch, SQL, data engineering, and version control; and good English plus at least B2 German for clinical collaboration. Experience with OMOP CDM or Target Trial methodology is an advantage.
The position is full-time, TV-L E13, for 36 months, starting November 2026. Funding also includes support for open-access publications, international conferences, research mobility grants, and TUM Graduate School support.
Applications are considered until the position is filled. The work location is Munich, Germany. Contact: Maximilian Tschochohei ([email protected]).
Funding details
Fully funded PhD position (E13 TV-L, full-time, 36 months). Funding includes support for open-access publications, international conferences and research mobility grants, plus TUM Graduate School support and employee benefits.
What's required
Excellent Master's degree in Computer Science, Medical Informatics, Health Informatics, Data Science, or a closely related field; strong academic record suitable for doctoral study at TUM; documented experience in machine learning and/or natural language processing with deep knowledge of LLMs, RAG and agent-based AI systems; strong Python skills and familiarity with ML frameworks such as PyTorch, data engineering, SQL and version control; interest in longitudinal clinical data, clinical terminologies and interoperable data models, with OMOP CDM or Target Trial methodology as an advantage; careful approach to evaluation, reproducibility, Responsible AI, uncertainty and patient safety; strong communication skills in English and German proficiency at least B2 for clinical collaboration.
How to apply
Apply through the TUM application process for the advertised PhD position. Prepare your application materials and submit them as soon as possible, since applications are considered until the position is filled. Contact Maximilian Tschochohei by email if you need clarification.
More information can be found here
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