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Field
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AI technologies. We develop innovative approaches that combine deep learning, computer vision, and bioinformatics to extract actionable insights from complex, multi-modal data, including medical
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that combine deep learning, computer vision, and bioinformatics to extract actionable insights from complex, multi-modal data, including medical imaging, genomics, and clinical records. A central theme of our
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, multidisciplinary research team focused on advancing cancer care through cutting-edge computational and AI technologies. We develop innovative approaches that combine deep learning, computer vision, and
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that combine deep learning, computer vision, and bioinformatics to extract actionable insights from complex, multi-modal data, including medical imaging, genomics, and clinical records. A central theme of our
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Full-time: 35 hours per week Fixed-term: 13 months The opportunity: Conduct original research on neuronal networks underlying the processing of natural visual stimuli, using in vivo large-scale two
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patients with aortic stenosis pre- and post-surgery. The post-holder will have a background in engineering, computing and / or image data analysis or equivalent and will be based in Leeds to support advanced
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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device
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to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
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undergoing weight loss, as well as knee cartilage thickness and strain using MRI, inflammatory biomarkers, and the synovial fluid microenvironment in a subgroup of individuals. It will utilise advanced imaging
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used in our work centre around optical imaging and spectroscopy and nanofabrication. The work also relies on theory and simulation, specifically focusing on numerical mean-field electrostatics