PhD Position (E13, 100%, 4+ years)

PhD Position: Large Multimodal Models for Digital Patient Twins (E13, 100%, 4 years)

30.07.2026, Academic staff

For our AI-Assisted Healthcare Lab at TUM School of Health and Medicine, we are seeking an outstanding PhD student to develop next-generation multimodal foundation models for digital patient twins in oncology and cardiovascular medicine. The position is part of the EU Horizon Europe project TWIN-X, Digital Twins with Generative AI for Explainable Precision Medicine, a consortium of 18 partners from 12 European countries. Full-time, TV-L E13, fixed-term for 48 months.

PhD Position in Foundation Models and Digital Patient Twins for Precision Medicine The Department of Diagnostic and Interventional Radiology at TUM University Hospital is recruiting a full-time PhD student (f/m/d) for the EU Horizon Europe project TWIN-X: Digital Twins with Generative AI for Explainable Precision Medicine.

Why this position is unique

This PhD position offers access to large-scale multimodal clinical data, high-end GPU resources, and integration into TWIN-X, an EU Horizon Europe consortium with 18 partners from 12 European countries.

You will work with data from TUM University Hospital and European partner institutions, including:

Radiological imaging
Digital pathology
Genomics
Laboratory values
Clinical notes and reports
Longitudinal patient trajectories from several thousand patients

The project has access to two new NVIDIA B300 servers, additional H100 and H200 GPU servers, and large-scale storage infrastructure. Compute capacity is continuously expanded to minimize bottlenecks.

The position includes funding for conference travel, collaboration with leading European research partners, and opportunities for short research stays at TWIN-X institutions in Greece, Italy, France, the Netherlands, Bulgaria, or Switzerland.

Research vision

The goal is to develop foundation-model architectures for digital patient twins in oncology and cardiovascular medicine. These models should learn patient representations across data types, organs, diseases, and time.

The models should capture:

Disease trajectories and prior medical history
Uncertainty and missing information
Signals relevant to diagnosis and prognosis
Potential treatment response

The project may include patient-level representations derived from imaging, pathology, genomics, laboratory values, clinical reports, and longitudinal events.

Potential methodological directions include:

Architectures for heterogeneous and asynchronous clinical data
Cross-attention models
Mixture-of-experts systems
Temporal transformers
JEPA-style architectures
Self-supervised and contrastive learning
Masked-modelling and generative pretraining objectives

Candidates are strongly encouraged to contribute and pursue their own research ideas.

Your responsibilities

Develop deep learning methods for multimodal and longitudinal patient modelling
Build and evaluate foundation models on large-scale clinical datasets
Work with radiology, pathology, genomics, laboratory, and clinical text data
Design clinically meaningful benchmarks and robust evaluation frameworks
Publish at leading machine learning and medical AI venues
Collaborate with clinicians, computer scientists, and European research partners
Contribute to TWIN-X deliverables and present research internationally

Your profile

We seek a candidate with exceptional analytical ability, excellent academic performance, and a strong technical background.

Master’s degree with excellent grades in computer science, mathematics, physics, engineering, medical informatics, biomedical engineering, or a related field
Very strong undergraduate and graduate academic record, particularly in quantitative subjects
Strong Python skills
Experience with deep learning frameworks, preferably PyTorch
Solid foundations in machine learning, statistics, linear algebra, and model evaluation
Interest in foundation models, representation learning, multimodal learning, generative AI, or longitudinal modelling
Ability to work independently and learn complex methods quickly
Excellent English-language skills

German-language skills and prior experience in medical AI are helpful but not required.

We offer

Full-time position according to TV-L E13 for 48 months
Opportunity to complete a PhD at TUM University Hospital and the Technical University of Munich
Access to large-scale multimodal clinical datasets
Access to high-end GPU and storage infrastructure
Integration into the TWIN-X consortium with 18 partners from 12 countries
Opportunities for short research stays at partner institutions
Funding for international conferences and workshops
Flexible working hours and options for remote work
Close collaboration with clinicians, AI researchers, and European partners

Supervision

The position is embedded in the medical AI research environment of TUM University Hospital and the Department of Diagnostic and Interventional Radiology.

Prof. Dr. Lisa Adams Professor of Radiology Deputy Director of Radiology, TUM University Hospital Google Scholar: profile

PD Dr. med. Keno Bressem Radiologist and Coordinator of the TWIN-X project TUM University Hospital Google Scholar: profile

Dr. rer. nat. Cosmin I. Bercea Senior Researcher in Generative AI and Medical Imaging TUM University Hospital Google Scholar: profile

Position details

Position: PhD Student, f/m/d Topic: Foundation Models and Digital Patient Twins for Precision Medicine Project: TWIN-X: Digital Twins with Generative AI for Explainable Precision Medicine Institution: Department of Diagnostic and Interventional Radiology, TUM University Hospital, Klinikum rechts der Isar Employment: Full-time Salary: TV-L E13 Duration: 48 months Location: Munich, Germany

Application

Please send your application by email to keno.bressem@tum.de or lisa.adams@tum.de

Please submit the following documents:

Cover letter
Curriculum vitae
Complete Bachelor’s and Master’s transcripts
Degree certificates
Publication list, if available
Code portfolio or GitHub profile, if available
Names and contact details of academic references, if available

Please include all undergraduate and graduate transcripts. Applications without complete transcripts cannot be fully assessed.

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:

When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art.13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: keno.bressem@tum.de, lisa.adams, cosmin.bercea@tum.de