Postdoctoral Researcher - Multimodal AI for Whole-Body PET/CT

UniBE is interconnected. With us, you're part of an international community and benefit from interdisciplinary collaboration.

Your responsibilities

• Shape research questions and develop multimodal methods combining PET/CT with clinical text, followed by longitudinal imaging and digitised histology.
• Develop context-aware image analysis and segmentation, multimodal representation learning and outcome prediction; explore physician-editable report generation.
• Lead projects from model design through multicentre clinical validation.
• Aim to publish in leading international journals and present at machine-learning and medical-imaging conferences.
• Contribute to follow-on grant applications and interdisciplinary collaboration.

Your profile

• PhD in computer science, machine learning, biomedical engineering or a related field.
• Strong deep-learning research on images; 3D/volumetric experience highly desirable.
• Excellent Python/PyTorch skills; reproducible pipelines at scale.
• Substantial methodological ownership and strong first-author publications.
• Ability to frame research questions, design baselines/ablations, identify leakage and shortcut learning, and communicate across disciplines.
• Particularly valuable: multimodal/vision-language learning, self-supervised pretraining or medical foundation models, longitudinal imaging, clinical NLP/report generation, computational pathology/whole-slide imaging, and distributed training.
• Prior PET/CT experience is not required; we provide clinical, biological and imaging expertise.

Application and Contact

Interested candidates are requested to send a one-page note identifying the research question you would most want to pursue here and how you would test it, a CV with publications, expected starting date, as well as contact information for two references or their reference letters, in one PDF file to [email protected].