PhD Position in Explainable Multimodal AI for Precision Oncology (xMDT-HPB Project) Technical University of Munich in Germany

The Institute for AI and Informatics in Medicine (AIIM) at the Technical University of Munich (TUM) is offering a fully funded PhD position within the xMDT-HPB project, a pioneering research initiative focused on developing explainable and interoperable multimodal AI systems for precision oncology. The project aims to improve personalized prognoses and treatment decisions for patients with hepato-pancreato-biliary cancers by integrating longitudinal clinical data, medical text narratives, multimodal radiological imaging, and laboratory trajectories. All research components will be released as open-source software and reusable research artifacts, contributing to the advancement of medical informatics and AI in clinical care.

As a doctoral candidate, you will join an interdisciplinary team and benefit from structured academic supervision, clear onboarding, and regular feedback. Your responsibilities include conducting your PhD research within the xMDT-HPB project, familiarizing yourself with clinical data and medical research questions, contributing to the design and implementation of research prototypes, and developing methods for explainable, multimodal AI systems. You will build and maintain reproducible experiments, benchmarking pipelines, and analyses, collaborate with clinicians, data scientists, and software engineers, and prepare research findings for internal meetings, scientific publications, and conferences. Additionally, you will contribute to open-source software, technical documentation, and quality assurance measures.

The ideal candidate will have a completed master’s degree in computer science, medical informatics, data science, bioinformatics, computational science, or a related field (degree by start date is sufficient). Solid programming skills in Python and experience with machine learning frameworks such as PyTorch or TensorFlow are required. Practical experience from a master's thesis, study projects, internships, or open-source projects in machine learning, NLP/LLM, data analysis, software development, or medical data processing is expected. Good written and spoken English and German language skills at least at level C1 are required for internal and clinical collaboration. Applicants should demonstrate a structured, learning-oriented approach, strong teamwork and interdisciplinary communication skills, and willingness to familiarize themselves with medical standards, data protection, reproducibility, and good scientific practice. A brief overview of previous programming, data analysis, or machine learning projects is requested; links to GitHub or GitLab are welcome. If available, a list of publications, posters, thesis abstracts, or other academic evidence should be included.

The position offers a scientifically ambitious project with high societal relevance, designed to build advanced research skills early in your academic career. You will work in an interdisciplinary and international environment at the intersection of AI research, medical informatics, and clinical care. Support for scientific publications, open-access publications, and conference attendance is provided, along with mentoring, qualification programs, and assistance in building your academic profile. The position is fully funded for 36 months, with remuneration according to TV-L EG 13 (100%), occupational pension scheme via VBL, and additional employer benefits such as EGYM Wellpass, Corporate Benefits, and access to university library services. The workplace is located in the heart of Munich at Max-Weber-Platz, offering excellent connections to public transport.

Applications are reviewed on a rolling basis until the position is filled. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude, and professional performance. Please submit your application as a single PDF to Dr. Felix Busch, including a motivation letter, CV, certificates and transcripts, overview of programming/data analysis/machine learning projects, and contact details of two academic references. For more information, visit the provided academic profile and application portal links.