Erstellt am 8. Juni 2026
PhD Position: Large Multimodal Models for Digital Patient Twins (E13, 100%, 4+ years)
Technische Universität München
München, Bavaria 80333, Germany
Vollzeit
Reference: 1069577820
PhD Position: Large Multimodal Models for Digital Patient Twins (E13, 100%, 4+ years)
06.06.2026, 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.
Why this position is unique
This PhD position offers 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 partners, including radiology, pathology, genomics, laboratory values, clinical notes, and longitudinal patient trajectories from several thousand patients.
The project has access to two new B300 servers, additional H100 and H200 GPU servers, and large-scale storage. Compute capacity is continuously expanded to avoid bottlenecks. The position includes conference travel, collaboration with leading European partners, and 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, capturing disease trajectory, prior history, uncertainty, missing information, and clinical signals for diagnosis, prognosis, and treatment response.
The project may include patient-level embeddings from imaging, pathology, genomics, laboratory values, reports, and longitudinal events, as well as architectures for heterogeneous and asynchronous clinical data, including cross-attention models, mixture-of-experts systems, temporal transformers, or JEPA-style models. Pretraining may use self-supervised, contrastive, masked-modelling, or generative objectives. Own research ideas are strongly encouraged.
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 evaluations
• Publish at leading machine learning and medical AI venues
• Collaborate with clinicians, computer scientists, and European partners
• Contribute to TWIN-X deliverables and present work 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 record, especially in quantitative subjects
• Strong Python skills and 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 difficult methods quickly
• Excellent English skills
German and prior experience in medical AI are helpful but not required.
We offer
• Full-time TV-L E13 position for 48 months
• PhD at TUM University Hospital and Technical University of Munich
• Access to large-scale multimodal clinical datasets and high-end GPU infrastructure
• Integration into the TWIN-X consortium with 18 partners from 12 countries
• 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 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 to [email protected] .
• Cover letter
• CV
• Complete Bachelor and Master transcripts
• Degree certificates
• Publication list, if available
• Code portfolio or GitHub profile, if available
• Names of 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: [email protected]
06.06.2026, 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.
Why this position is unique
This PhD position offers 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 partners, including radiology, pathology, genomics, laboratory values, clinical notes, and longitudinal patient trajectories from several thousand patients.
The project has access to two new B300 servers, additional H100 and H200 GPU servers, and large-scale storage. Compute capacity is continuously expanded to avoid bottlenecks. The position includes conference travel, collaboration with leading European partners, and 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, capturing disease trajectory, prior history, uncertainty, missing information, and clinical signals for diagnosis, prognosis, and treatment response.
The project may include patient-level embeddings from imaging, pathology, genomics, laboratory values, reports, and longitudinal events, as well as architectures for heterogeneous and asynchronous clinical data, including cross-attention models, mixture-of-experts systems, temporal transformers, or JEPA-style models. Pretraining may use self-supervised, contrastive, masked-modelling, or generative objectives. Own research ideas are strongly encouraged.
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 evaluations
• Publish at leading machine learning and medical AI venues
• Collaborate with clinicians, computer scientists, and European partners
• Contribute to TWIN-X deliverables and present work 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 record, especially in quantitative subjects
• Strong Python skills and 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 difficult methods quickly
• Excellent English skills
German and prior experience in medical AI are helpful but not required.
We offer
• Full-time TV-L E13 position for 48 months
• PhD at TUM University Hospital and Technical University of Munich
• Access to large-scale multimodal clinical datasets and high-end GPU infrastructure
• Integration into the TWIN-X consortium with 18 partners from 12 countries
• 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 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 to [email protected] .
• Cover letter
• CV
• Complete Bachelor and Master transcripts
• Degree certificates
• Publication list, if available
• Code portfolio or GitHub profile, if available
• Names of 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: [email protected]
