PhD Position in Radiology (Technical University of Munich)

The Department of Diagnostic and Interventional Radiology at Klinikum rechts der Isar, Technical University of Munich, is seeking a PhD Student (f/m/d) for multimodal embeddings and fusion architectures for digital patient twins in the EU research project TWIN-X. Position details Position: PhD Student (f/m/d) Topic: Multimodal embeddings and fusion architectures for digital patient twins Project: TWIN-X, Digital Twins with Generative AI for Explainable Precision Medicine Institution: Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich Employment: Full-time Salary: TV-L E13 Duration: Fixed-term for 48 months About TWIN-X TWIN-X is a European Commission-funded research project under Horizon Europe. The consortium includes 18 partners from 12 European countries. The project aims to develop digital patient twins by fusing multimodal clinical data into unified and explainable vector representations for clinical decision support in oncology and cardiovascular medicine. Data modalities include: Radiology Pathology Genomics Laboratory values Clinical notes The position is embedded in the core technical method development of TWIN-X and focuses on multimodal representation learning and the fusion of heterogeneous patient data. Research focus The PhD project may include the following areas: Multimodal representation learning Development of embedding models that map medical imaging, histopathology, genomics, laboratory data, and clinical text into a shared and explainable vector space. Fusion architectures Design and evaluation of modern fusion methods, such as cross-attention, mixture-of-experts models, and transformer-based multimodal models, for integrating heterogeneous and incomplete patient data. Self-supervised and contrastive learning Pre-training of multimodal foundation models on large clinical datasets. Explainability and robustness Development of methods to make learned representations interpretable, robust, and trustworthy for clinical use. Clinical validation Application and evaluation of the developed models in oncological and cardiovascular use cases together with clinical partners. The exact focus will be determined together with the supervisor and can be tailored to the applicant’s interests and expertise. Your responsibilities Independent research in multimodal representations and digital patient twins Development and implementation of deep learning methods for fusing heterogeneous clinical data Publication of research results at international conferences and in peer-reviewed journals Collaboration with clinicians, data scientists, and partners across the EU consortium Contribution to project deliverables and milestone reports Potential supervision of student theses Your profile Master’s degree with very good to excellent grades in Computer Science, Medical Informatics, Physics, Mathematics, Biomedical Engineering, or a related field Strong programming skills in Python and experience with deep learning frameworks, preferably PyTorch Interest in multimodal AI, representation learning, medical imaging, or natural language processing Excellent English skills German is a plus but not required Self-motivation, teamwork skills, and enthusiasm for interdisciplinary research Preferred qualifications Experience with multimodal models, vision-language models, or foundation models Experience with scientific writing and publishing Experience with medical datasets or clinical research We offer Remuneration according to TV-L E13, full-time Fixed-term position for the project duration of 48 months PhD at TUM, one of Europe’s leading technical universities Access to extensive clinical datasets and high-performance computing infrastructure, including GPU clusters Integration into an international research network with 18 partners Opportunity to attend international conferences and workshops Flexible working hours and the option for remote work Application Please send your complete application documents by email. Application documents should include: Cover letter CV Transcripts Publication list, if available Code portfolio or GitHub profile, if available Contact Prof. Dr. Lisa Adams PD Dr. med. Keno Bressem Email: keno.bressem@tum.de Equal opportunity statement TUM is committed to increasing the proportion of women in its workforce. Applications from women are therefore expressly encouraged. Candidates with disabilities who are otherwise equally qualified will be given preference. Die Stelle ist für die Besetzung mit schwerbehinderten Menschen geeignet. Schwerbehinderte Bewerberinnen und Bewerber werden bei ansonsten im wesentlichen gleicher Eignung, Befähigung und fachlicher Leistung bevorzugt eingestellt. Data protection notice Hinweis zum Datenschutz: Im Rahmen Ihrer Bewerbung um eine Stelle an der Technischen Universität München (TUM) übermitteln Sie personenbezogene Daten. Beachten Sie bitte hierzu unsere Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. Durch die Übermittlung Ihrer Bewerbung bestätigen Sie, dass Sie die Datenschutzhinweise der TUM zur Kenntnis genommen haben. Kontakt: keno.bressem@tum.de If you apply for this position please say you saw it on Academicoxy

The Department of Diagnostic and Interventional Radiology at Klinikum rechts der Isar, Technical University of Munich, is seeking a PhD Student (f/m/d) for multimodal embeddings and fusion architectures for digital patient twins in the EU research project TWIN-X.

Position details

Position: PhD Student (f/m/d)
Topic: Multimodal embeddings and fusion architectures for digital patient twins
Project: TWIN-X, Digital Twins with Generative AI for Explainable Precision Medicine
Institution: Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich
Employment: Full-time
Salary: TV-L E13
Duration: Fixed-term for 48 months

About TWIN-X

TWIN-X is a European Commission-funded research project under Horizon Europe. The consortium includes 18 partners from 12 European countries.

The project aims to develop digital patient twins by fusing multimodal clinical data into unified and explainable vector representations for clinical decision support in oncology and cardiovascular medicine.

Data modalities include:

Radiology
Pathology
Genomics
Laboratory values
Clinical notes

The position is embedded in the core technical method development of TWIN-X and focuses on multimodal representation learning and the fusion of heterogeneous patient data.

Research focus

The PhD project may include the following areas:

Multimodal representation learning
Development of embedding models that map medical imaging, histopathology, genomics, laboratory data, and clinical text into a shared and explainable vector space.
Fusion architectures
Design and evaluation of modern fusion methods, such as cross-attention, mixture-of-experts models, and transformer-based multimodal models, for integrating heterogeneous and incomplete patient data.
Self-supervised and contrastive learning
Pre-training of multimodal foundation models on large clinical datasets.
Explainability and robustness
Development of methods to make learned representations interpretable, robust, and trustworthy for clinical use.
Clinical validation
Application and evaluation of the developed models in oncological and cardiovascular use cases together with clinical partners.

The exact focus will be determined together with the supervisor and can be tailored to the applicant’s interests and expertise.

Your responsibilities

Independent research in multimodal representations and digital patient twins
Development and implementation of deep learning methods for fusing heterogeneous clinical data
Publication of research results at international conferences and in peer-reviewed journals
Collaboration with clinicians, data scientists, and partners across the EU consortium
Contribution to project deliverables and milestone reports
Potential supervision of student theses

Your profile

Master’s degree with very good to excellent grades in Computer Science, Medical Informatics, Physics, Mathematics, Biomedical Engineering, or a related field
Strong programming skills in Python and experience with deep learning frameworks, preferably PyTorch
Interest in multimodal AI, representation learning, medical imaging, or natural language processing
Excellent English skills
German is a plus but not required
Self-motivation, teamwork skills, and enthusiasm for interdisciplinary research

Preferred qualifications

Experience with multimodal models, vision-language models, or foundation models
Experience with scientific writing and publishing
Experience with medical datasets or clinical research

We offer

Remuneration according to TV-L E13, full-time
Fixed-term position for the project duration of 48 months
PhD at TUM, one of Europe’s leading technical universities
Access to extensive clinical datasets and high-performance computing infrastructure, including GPU clusters
Integration into an international research network with 18 partners
Opportunity to attend international conferences and workshops
Flexible working hours and the option for remote work

Application

Please send your complete application documents by email.

Application documents should include:

Cover letter
CV
Transcripts
Publication list, if available
Code portfolio or GitHub profile, if available

Contact

Prof. Dr. Lisa Adams
PD Dr. med. Keno Bressem
Email: keno.bressem@tum.de

Equal opportunity statement

TUM is committed to increasing the proportion of women in its workforce. Applications from women are therefore expressly encouraged. Candidates with disabilities who are otherwise equally qualified will be given preference.

Die Stelle ist für die Besetzung mit schwerbehinderten Menschen geeignet. Schwerbehinderte Bewerberinnen und Bewerber werden bei ansonsten im wesentlichen gleicher Eignung, Befähigung und fachlicher Leistung bevorzugt eingestellt.

Data protection notice

Hinweis zum Datenschutz:
Im Rahmen Ihrer Bewerbung um eine Stelle an der Technischen Universität München (TUM) übermitteln Sie personenbezogene Daten. Beachten Sie bitte hierzu unsere Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. Durch die Übermittlung Ihrer Bewerbung bestätigen Sie, dass Sie die Datenschutzhinweise der TUM zur Kenntnis genommen haben.

Kontakt: keno.bressem@tum.de

If you apply for this position please say you saw it on Academicoxy

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