Identifying sub-populations of cells critical for cancer disease progression

Tissue multiplexing is a new imaging method that allows to visualise a large number of protein targets in tissues. This exciting new technology allows for new approaches to phenotyping cells and to decode more complex patters of communication between different tissue compartments. The goal of this project is to develop the required image analysis and inference methods using advanced machine learning and AI in 2D and 3D. As a result, you will be advancing our understanding of the tumour environment and find novel ways of identifying sub-populations of cells that play a critical role in disease progression.

You will be working side by side with world leading cancer researchers at NIH NCI and the University of Oxford. At both sites you will have access to unique patient cohorts. Together with David Wink and Stephen Lockett (both NCI) you will be working on aspects if breast cancer. In Oxford, Richard Bryant and Ian Mills will lead on work in prostate cancer, which is the commonest non-cutaneous cancer in men, and often progresses to incurable metastatic disease. Your work will also be supported by expert pathologists and you will be working towards improving current practice in cellular pathology.

The broader group has already established a very active collaboration and you will be expected to work in both locations. In Oxford, you will be embedded in the Quantitative Biomedical Image Analysis group led by Prof. Rittscher. Part of your role will be to accelerate the exchange of technology and software between the two locations. This project provides a unique opportunity to study mechanisms that are common to different cancer types.