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

Tasks

• 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.

Requirements

• 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.

Benefits

• Meaningful work and fair compensation
• Strong research infrastructure and international network
• Interdisciplinary collaboration
• Access to cutting-edge technology and innovation

Application

Interested candidates are requested to send a cover letter describing current and future research interests, a CV with publications, expected starting date, as well as contact information for two references or their reference letters, in one PDF file to robert.seifert@insel.ch.