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

UniBE is a research-focused institution that values interdisciplinary collaboration. The role of Postdoctoral Researcher in Multimodal AI for Whole-Body PET/CT involves developing advanced AI methods to enhance medical imaging and clinical outcomes. The main responsibilities include shaping research questions, developing multimodal methods that combine PET/CT with clinical text, and creating context-aware image analysis and segmentation techniques. The role also involves leading projects from model design through multicentre clinical validation, aiming to publish in leading journals and present at conferences. The researcher will contribute to follow-on grant applications and interdisciplinary collaboration. The ideal candidate will have a PhD in computer science, machine learning, biomedical engineering, or a related field. Strong deep-learning research experience, particularly with 3D/volumetric imaging, is essential. Excellent Python/PyTorch skills and the ability to create reproducible pipelines at scale are required. The candidate should have substantial methodological ownership and strong first-author publications. Experience in multimodal/vision-language learning, self-supervised pretraining, or medical foundation models is particularly valuable. The role does not require prior PET/CT experience, as the team provides clinical, biological, and imaging expertise. What the role asks for: - PhD in computer science, machine learning, biomedical engineering or a related field. - Strong deep-learning research on images. - Excellent Python/PyTorch skills. - Reproducible pipelines at scale. - Substantial methodological ownership. - Strong first-author publications. - Ability to frame research questions, design baselines/ablations, identify leakage and shortcut learning, and communicate across disciplines. - Nice-to-have: multimodal/vision-language learning, self-supervised pretraining or medical foundation models.