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symbiosis of cutting-edge AI combined with human support. The successful candidate will partake in the development of personalised, context-sensitive Large Language Models (LLMs) for health coaching. By
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, epidemiology, health data science, or other closely related disciplines. They will bring internationally recognised expertise in statistics and methodological leadership in digital trials, demonstrated by a
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or Computer Science. Confident in data analysis, including handling large data sets using relevant software, such as Power BI, Tableau, Python, SQL, SSRS, R, Stata, SPSS, or Excel Able to extract, transform and combine
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-resolution spatial transcriptomics and multiplex imaging, to turn large-scale data into actionable insights for both fundamental and translational research. About the role We are seeking a Senior
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cycle, leveraging the advanced capabilities of Generative AI and Large Language Models. Our goal is to deliver innovative methods and tools that generate software artifacts (e.g., requirements, code, and
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for leading large-scale digital health research projects Strong understanding of healthcare data standards (e.g., FHIR, HL7) and regulatory requirements Leadership experience in managing multidisciplinary teams
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optimisation of recruitment strategies in line with core King’s principles. The ideal candidate will have proven experience in market research and data analysis leadership positions, preferably in large and
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at meetings Experience in developing deep learning models Ability to manage own academic research and associated activities Desirable criteria Experience in dealing with large image data Experience
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at the intersection of obstetrics and imaging. The successful candidate will play a key role in recruiting and supporting pregnant participants, coordinating MRI scans, and inputting into data collection and analysis
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software platforms for electronic healthcare record research and direct care that includes Natural Language Processing / Large Language Model (LLM) capability, e.g. extracting structured information from