PhD position in computational pathology and image analysis; -RE

PhD position in computational pathology and image analysis

The Institute of Pathology, at the Lausanne University Hospital (CHUV) is opening a PhD Position for a start date on April 1st 2026:

The Lausanne University Hospital (CHUV) is one of five Swiss university hospitals. Through its collaboration with the Faculty of Biology and Medicine of the University of Lausanne and EPFL, CHUV plays a leading role in the areas of medical care, medical research, and training.

We are inviting applications for a fully funded PhD position focused on computational pathology
, with a specific emphasis on the analysis of whole-slide images (WSIs) to distinguish between multiple primary lung tumors and metastatic disease. The successful candidate will join a dynamic interdisciplinary team working at the intersection of pathology, computational science, and machine learning.

This research will contribute to improving diagnostic accuracy in thoracic oncology by developing novel image‑based computational methods to support decision‑making in challenging clinical scenarios where multiple lung lesions are present. The project will leverage digital histopathology images and associated clinical metadata to build robust, interpretable algorithms for tumor classification, comparison, and characterization.

The PhD student will be co‑mentored by faculty with expertise in pathology, machine learning, and biomedical image analysis, and will have the opportunity to contribute to high‑impact publications and translational research.

Develop image analysis algorithms to differentiate multiple primary lung tumors from intrapulmonary metastases using WSIs

• Extract and quantify relevant histological features from tumor regions using deep learning and classical image processing
• Design computational experiments to validate and benchmark algorithms on expert‑annotated datasets
• Contribute to the development of interpretable tools that integrate into clinical workflows and support precision diagnostics

• Preprocess large histopathology datasets (e.g., stain normalization, tiling, artifact removal)
• Collaborate with pathologists for region annotation and ground‑truth labeling

• Design and implement machine learning pipelines (e.g., CNNs, graph‑based models, patch‑level feature extractors) for tumor differentiation
• Develop similarity metrics or learned embeddings for comparing tumors across slides or within patients
• Conduct rigorous experiments to assess diagnostic accuracy, reproducibility, and generalizability of models
• Analyze results statistically and in collaboration with clinical experts

• Contribute to the writing of scientific manuscripts, conference presentations, and progress reports
• Participate in interdisciplinary research meetings and collaborative planning sessions
• Develop modular, well‑documented code and interfaces to facilitate use by other researchers or collaborators
• Mentor undergraduate students or junior lab members on aspects of data preparation and model development

• Stay current with state‑of‑the‑art literature in computational pathology, deep learning, and lung cancer diagnostics
• Attend scientific conferences and training workshops

• Master’s degree in Computer Science, Biomedical Engineering, Electrical Engineering, Applied Mathematics, or a related field
• Strong programming proficiency in Python and experience with scientific computing libraries (e.g., Num Py, Sci Py, scikit-image)
• Experience working with digital histopathology data or other large-scale imaging datasets
• Familiarity with deep learning frameworks (e.g., PyTorch or Tensor Flow)
• Prior work involving classification, segmentation, or similarity analysis in medical imaging or histology
• Experience with whole slide image handling libraries (e.g., Open Slide, Slide
  
  IO)
• Understanding of histopathological features relevant to lung cancer (e.g., tumor architecture, cell morphology)
• Excellent communication skills and the ability to work in a collaborative, multidisciplinary setting.

To become an employee of the world‑famous University Hospital Center from the…