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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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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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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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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
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following skills and experience: Essential criteria Demonstrable experience of managing a range of HR and employee relations issues within a large complex organisation Up to date knowledge of employment law
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About us The Department of War Studies has a large and diverse faculty. We are a theoretically and methodologically diverse Department with expertise in areas including, but not limited to, conflict
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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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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