87 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" research jobs at KINGS COLLEGE LONDON
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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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establishing a strong academic track record. You may have worked in MRI research previously or have strong computational / AI / machine learning skills used in other areas of research. Essential criteria PhD
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on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools
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. We use this expertise to teach the next generation of health professionals and research scientists. Based across Guy’s, St Thomas’ and Waterloo campuses, King’s Denmark Hill, our academic programme of
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(Pharmacy, 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
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are seeking a postdoctoral research associate to lead an innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a
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King's scientists. Many of the research groups were top rated in the last HEFCE Research Assessment Exercise. The Faculty’s students enjoy unrivalled learning opportunities, supported by strong
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significant growth with substantial investment in new appointments, research infrastructure and laboratory space refurbishment. Further information may be found at: http://www.kcl.ac.uk/physics About the role
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world data, with strong programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven