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vision and machine learning methods for multimodal imaging and real-time analysis in colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection
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About Us The Faculty of Life Sciences & Medicine is one of the largest and most successful centres for research and education in the UK. The Faculty was created as a result of the merger of elements
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observational data, and compare the results with those from other emulators of similar datasets (e.g. Gaussian Process methods by the project lead). These results will inform the IPCC AR7, and adaptation and
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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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. Suggesting further appropriate methods and analyses. Writing manuscripts in to disseminate research findings. Present summaries of study updates at internal meetings and meetings with external collaborators
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well as statistical inferences on model outputs, for pre specified research hypotheses. Suggesting further appropriate methods and analyses. Writing manuscripts in to disseminate research findings. Present summaries
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these projections with observational data, and compare the results with those from other emulators of similar datasets (e.g. Gaussian Process methods by the project lead). These results will inform the IPCC AR7, and
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an interdisciplinary team of researchers as well as the Centre’s academic, lived-experience, and community partners. Additionally, you will work with researchers in the Centre’s Theory & Methods Hub and Communications
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using a range of methods including cutting-edge single cell and imaging techniques. Ultimately this information will provide fundamental insights into human biology and may in the future lead to improved
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have lived experience of slavery, trafficking or exploitation Experience with transfer of knowledge and methods between areas or disciplines Knowledge on intersectionality and identity of victims