Über die Stelle
UniBE is interconnected.
With us, you’re part of an international community and benefit from interdisciplinary collaboration.
Aufgaben
- 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.
Anforderungen
- 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.
Ausbildung
University
Benefits
Meaningful work and fair compensation
Um dich für diesen Job zu bewerben, besuche bitte jobs.unibe.ch.