Universität Bern

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Who are we?

The Odyssey Digital Pathology Research Group at the University of Bern (Group Prof. I. Zlobec) takes a deep dive into the morphomolecular aspects and spatial biology of colorectal cancer using various computational, bioinformatics and tissue visualization techniques to gain insights into colorectal cancer biology, metastatic dissemination as well as to identify new predictive and prognostic (spatial) biomarkers. We use digital pathology and artificial intelligence (AI) to investigate the multi-faceted phenomenon of "tumor budding" and the tumor microenvironment. Our group is enriched by the multi-disciplinary background of our students, our numerous industry partners as well as national and international academic collaborators spanning the areas of pathology, immunology, oncology, machine learning and computer vision. For more information, please visit our website here: . Start: November 1st, 2025 (negotiable)

Who are we looking for:

We are seeking a full-time Postdoctoral candidate for a 2-year ISREC-TANDEM funded project focused on the development, validation, and deployment of a multi-cancer lymph node screening tool in the prospective clinical settings.This project bridges cutting-edge image analysis with clinical translation, leveraging state-of-the-art foundation models to support pathology practice. The candidate will work at the interface of basic research and clinical application, contributing to the translation of research innovations into the clinic.

Your profile:

• PhD in computer science, medical imaging, engineering, biomedical sciences, or MD degree with experience in medical image analysis and computer vision
• Strong background in image analysis, digital/computational pathology
• Expertise in machine learning / deep learning (preferably with foundation models)
• Strong motivation and translation mindset for digital/computer-assisted pathology
• Proficiency in Python (and relevant libraries such as PyTorch, TensorFlow, etc.)
• Familiarity with cloud computing platforms (e.g., AWS, Azure) and HPC
• Familiarity with digital pathology platforms (e.g., HaloAP, QuPath)
• Strong skills in data management, validation, and integration of deep learning workflows
• Experience working in interdisciplinary or translational research environments
• Track record of scientific publications in relevant fields
• Excellent communication skills (written and spoken) in English; German is an asset
• Ability to solve problems by taking own initiative and working independently
• Ability to collaborate effectively with both clinicians and basic researchers
• Motivation to work in a clinical research translation environment
• Passion for science and interest in contributing to discoveries in medicine & pathology
• Eager to be part of a group and contribute positively to team dynamics
• Willing to help supervise Master's and PhD students and contribute to lectures or workshops

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