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corresponding imaging data. Our partnership with the UCLH Biomedical Research Centre and NIHR Hearing Health Informatics Collaborative provides a strong foundation for translational research. We value technical
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Leedham (colorectal cancer biology), Dan Woodcock (cancer genomics), Helen Byrne (mathematical modelling), and Jens Rittscher (computational pathology and imaging AI), offering a unique opportunity to work
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solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated and real clinical scenarios. Evaluation may involve quantitative studies
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validate mechanical and electronic systems for image-guided therapy. Integrate pioneering and proven tools for the precise control and validation of interventional device placements. Examine clinical
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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing
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collaboration with the Translational Gastroenterology Unit (TGU) and the Ludwig Institute of Cancer Research (LICR) we aim to develop a computer guided endoscopy image recognition system that will support
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, chemists, computer scientists and biologists all working to develop imaging techniques within a supportive and diverse environment. Key Responsibilities This role will involve the operation of a new
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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prototype Total Factor Productivity (TFP) measure that includes non-market agricultural outputs and impacts beyond environmental indicators. The ideal candidate will hold a PhD in agricultural economics