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

University of Bern; Bern; Vor Ort; CHF 90.000 - 130.000; Vollzeit

Zusammenfassung

University of Bern in Bern seeks a computational imaging researcher to shape research questions and develop multimodal methods combining PET/CT with clinical text, followed by longitudinal imaging and digitised histology. You will lead projects from model design through multicentre clinical validation and aim to publish in leading journals.

You will develop context-aware image analysis and segmentation, multimodal representation learning and outcome prediction; explore physician-editable report

Qualifikationen

• PhD in computer science, machine learning, biomedical engineering or related field.
• Strong deep-learning research on images; 3D/volumetric experience 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.

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.

Jobbeschreibung

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.