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

Kanton Bern; Bern; Vor Ort; CHF 120.000 - 160.000; Vollzeit

Benefits dieser Stelle

Meaningful work

Strong research infrastructure

Interdisciplinary collaboration

Access to cutting-edge technology

Zusammenfassung

Kanton Bern seeks a PhD-level researcher to advance multimodal AI for whole-body PET/CT, integrating imaging with clinical context to distinguish residual malignancy from inflammation. You will lead methods development, validation across centres, and aim for high-impact publications.

We value strong Python/PyTorch skills, 3D imaging experience, and the ability to frame research questions and communicate across disciplines. Hybrid collaboration and cutting-edge resources are offered.

Qualifikationen

• PhD in computer science, ML, biomedical engineering or related field.
• Strong deep-learning research on medical imaging with 3D/volumetric experience.
• Excellent Python and PyTorch skills with scalable pipelines.

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 ML and medical-imaging conferences.
• Contribute to follow-on grant applications and interdisciplinary collaboration.

Kenntnisse

Deep learning research

Python

PyTorch

Cross-disciplinary communication

Ausbildung

PhD in computer science / ML / biomedical engineering or related field

Tools

Python

PyTorch

Jobbeschreibung

The project

With 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.

The project

With 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.

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.

Your Benefits

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