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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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associate with expertise in data science to join the King’s BHF Centre of Research Excellence and contribute to a growing cardio-immunology research programme. Inflammation is increasingly recognised as a key
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Knowledge, Skills and Experience Proven experience with machine learning model design and implementation, particularly in computer vision tasks (image classification, object detection) Strong programming
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processes that underly normal and abnormal cardiovascular and metabolic function and drive the translation of this strong basic science into advances in clinical practice. Our community of world-renowned
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-world conditions to verify system operation against targets and demonstrate the reliability of the technology for use in backup power, grid stabilisation, and renewable energy integration applications
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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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skills. Main duties will include: conduct tissue-mechanical and imaging experiments using early avian embryos; acquire and process data; prepare reagents and samples; optimise protocols; program and debug
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computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying statistical and machine learning to real life problems, using a
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departments: Cardiovascular Imaging, Cancer Imaging, Early Life Imaging, Imaging Chemistry & Biology, Biomedical Computing, Surgical & Interventional Engineering, Imaging Physics & Engineering and Digital Twins
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of Biomedical Engineering and Imaging Sciences is a cutting-edge research and teaching School dedicated to development, translation and clinical application within medical imaging and computational modelling