43 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" uni jobs at KINGS COLLEGE LONDON
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/ rhea.sookdeosingh@kcl.ac.uk Where to apply Website https://www.timeshighereducation.com/unijobs/listing/405793/lived-experience-ad… Requirements Additional Information Work Location(s) Number of offers
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Date: 20-Jan-2026 Contact Person: Gerome Breen / Anna Carnegie Contact Details: gerome.breen@kcl.ac.uk / anna.carnegie@kcl.ac.uk Where to apply Website https://www.timeshighereducation.com/unijobs
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Allowance per annum Job ID: 119706 Close Date: 11-Jan-2026 Contact Person: Psychosis Admin Team Contact Details: psychosis-admin@kcl.ac.uk Where to apply Website https://www.timeshighereducation.com/unijobs
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: 134845 Close Date: 19-Jan-2026 Contact Person: Marcelo Salierno Contact Details: marcelo.salierno@kcl.ac.uk Where to apply Website https://www.timeshighereducation.com/unijobs/listing/405742/sustainable
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About us We are looking for a flexible, organised and collaborative colleague to organise deliver research fellowship funding opportunities and learning and development programmes for health
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the globe The largest dental academic centre in the UK, we teach over 1,000 undergraduate and postgraduate taught students, and over 100 graduate research students. As well as undertaking and actively
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About us The King’s Health Partners Clinical Trials Office is a collaboration between King’s College London, Guy’s & St Thomas’ NHS Foundation Trust, King’s College Hospital NHS Foundation Trust and
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division, dedicated to the development, clinical translation, and clinical application of medical imaging and computational modelling technologies. The School is placed within St. Thomas’ Hospital and
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to the development of radio, television, mobile phones and radar. King's College London and Guy's and St Thomas', King's College Hospital and South London and Maudsley NHS Foundation Trusts are part of King's Health
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are caused and how people respond to treatments. We will investigate blood and cerebrospinal fluid from patients and use machine-learning to identify the treatment-relevant immune processes, with an emphasis