ph3The project /h3pWith university affiliation through the DBMR, we develop multimodal AI for whole-body PET/CT, integrating imaging with clinical context to distinguish residual malignancy from inflammation or physiological uptake. The focus is lymphoma response assessment, with methods designed to transfer across tasks, diseases and institutions. You will work with over 14,000 available PET/CT examinations from Bern (30 TB), linked reports, referrals and longitudinal studies, multicentre trial cohorts, and digitised histology in selected patients. /ph3The project /h3pWith university affiliation through the DBMR, we develop multimodal AI for whole-body PET/CT, integrating imaging with clinical context to distinguish residual malignancy from inflammation or physiological uptake. The focus is lymphoma response assessment, with methods designed to transfer across tasks, diseases and institutions. You will work with over 14,000 available PET/CT examinations from Bern (30 TB), linked reports, referrals and longitudinal studies, multicentre trial cohorts, and digitised histology in selected patients. /ph3Your Responsibilities /h3ulliShape research questions and develop multimodal methods combining PET/CT with clinical text, followed by longitudinal imaging and digitised histology. /liliDevelop context-aware image analysis and segmentation, multimodal representation learning and outcome prediction; explore physician-editable report generation. /liliLead projects from model design through multicentre clinical validation. /liliAim to publish in leading international journals and present at machine-learning and medical-imaging conferences. /liliContribute to follow-on grant applications and interdisciplinary collaboration. /li /ulh3Your profile /h3ulliPhD in computer science, machine learning, biomedical engineering or a related field. /liliStrong deep-learning research on images; 3D/volumetric experience highly desirable. /liliExcellent Python/PyTorch skills; reproducible pipelines at scale. /liliSubstantial methodological ownership and strong first-author publications. /liliAbility to frame research questions, design baselines/ablations, identify leakage and shortcut learning, and communicate across disciplines. /liliParticularly 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. /liliPrior PET/CT experience is not required; we provide clinical, biological and imaging expertise. /li /ulh3Your Benefits /h3ulliMeaningful work and fair compensation /liliStrong research infrastructure and international network /liliInterdisciplinary collaboration /liliAccess to cutting-edge technology and innovation /li /ul /p #J-18808-Ljbffr
Postdoctoral Researcher - Multimodal AI for Whole-Body PET/CT Arbeitgeber: Kanton Bern
Die Universität Bern ist ein hervorragender Arbeitgeber, der eine inspirierende Arbeitsumgebung bietet, in der Teamarbeit und persönliche Entwicklung gefördert werden. Als Teamleiter im Bereich Administration & Debitoren-Management profitieren Sie von flexiblen Arbeitszeiten, umfangreichen Weiterbildungsmöglichkeiten und einer wertschätzenden Unternehmenskultur, die Vielfalt und Innovation schätzt. Die zentrale Lage der Universität ermöglicht zudem eine gute Erreichbarkeit und ein lebendiges Umfeld für alle Mitarbeitenden.
