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including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and predictive analysis for Novartis Oxford collaboration for AI in medicine. The collaboration
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collaborative programme bringing together a team of leading experts in advanced electron microscopy imaging, first-principles modelling, metal halide semiconductor thin-film and device fabrication, and
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Generative Modelling to apply and develop cutting-edge deep generative probabilistic models including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and
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of Cambridge, UK (http://www.abg.psychol.cam.ac.uk ). The position will focus on neuroimaging and neurocomputational studies of learning and brain plasticity. Our studies combine ultra-high field brain 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
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at barrier surfaces. The work will combine advanced flow cytometry, immunohistochemistry, functional antimicrobial assays, confocal imaging and molecular techniques, alongside state-of-the-art imaging
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-national ERC Synergy Programme, EndoTheranostics, aiming to revolutionise Colorectal Cancer Treatment. The post-holder will work in the School of Biomedical Engineering & Imaging Sciences, King’s College
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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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biophysics, and cutting-edge analytical technologies. Your expertise in optical microscopy, single molecule imaging, computational imaging and data analysis will help us further develop mass photometry and
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. Experience in high content imaging, immunohistochemistry, W-blot, PCR, bulk and/or single cell RNAseq and in generating and analysing ‘omics data would be desirable. Diversity Committed to equality and valuing