16 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" research jobs at University of London in United Kingdom
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the design, development, deployment and evaluation of NeoShield’s applied machine-learning systems, the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis and the real-time ward
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:38183983) using cutting-edge genomics technologies (https://www.biorxiv.org/content/10.1101/2024.12.20.629444v5) to provide core knowledge for development of new therapies and management strategies for PPK
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pathways involved in kidney disease. They should be eager to learn and implement new experimental techniques, contribute to the day-to-day management of the laboratory, and take ownership of laboratory
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Music Centre and St Paul’s Cathedral. About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable. Throughout our history, we’ve
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& International Health is seeking to appoint a Research Fellow in Health Data Science (with a focus on machine learning) to NeoShield , a multi-country implementation research programme focused on neonatal sepsis
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The University of London The University of London is both the UK’s largest provider of international distance and online learning and the convenor of a federation of 17 renowned higher education
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About the Role This post focuses on applying advanced causal inference and machine learning methodologies to disentangle complex pathways between exposures and outcomes. By integrating multi-modal
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search strategies and lexicons. Proficiency in fitting and validating statistical models or machine learning algorithms is essential, along with advanced skills in R and/or Python for data processing and
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About the Role This is an exciting position where applicants are invited to join a multi-disciplinary team of bioengineers, biomedical scientists, and computer scientists working together at Queen
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About the Role This is an exciting position where applicants are invited to join a multi-disciplinary team of bioengineers, biomedical scientists, and computer scientists working together at Queen