39 machine-learning "https:" "https:" "https:" "https:" "U.S" research jobs at King's College London
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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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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automatic (potentially learned) strategies to search for optimal code transformations. To enable such a search, we contribute to open-source compilers such as LLHD/CIRCT, develop constraint programming
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worked in MRI research previously or have strong computational / AI / machine learning skills used in other areas of research. Essential criteria PhD qualified in relevant subject area Ability to work as
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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research
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(https://doi.org/10.1242/jcs.180703) and Prof. Philippa Francis-West (Mao et al. Nature Comms. 2016 (https://doi: 10.1038/ncomms11469 and Prof. Philippa Francis-West (Mao et al. Nature Comms. 2016 (https
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the AIMS-2-TRIALS research consortium (https://aims-2-trials.eu ) – the world’s largest autism research consortium - to coordinate work that will produce and promote impact from this project at a European
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Exercise. The Faculty’s students enjoy unrivalled learning opportunities, supported by strong partnerships with industry and healthcare organisations. At both undergraduate and postgraduate level, students
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recognised scientists and clinicians from across the School and King’s College London. More information: https://www.kcl.ac.uk/scmms About The Role This is an exciting opportunity for a Postdoctoral Research
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’ Hospital in central London in the School of Biomedical Engineering & Imaging Sciences at King’s College London: https://www.kcl.ac.uk/bmeis . There is an unmatched infrastructure in place to support the