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or related discipline to develop their data analysis and modelling skills and experience, applied to the field of health economics. We wish to encourage people who have strong quantitative skills who may be
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working full-time. An exciting opportunity has arisen for a talented graduate in a quantitative STEM, psychology, health or social sciences or related discipline to develop their data analysis and modelling
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offers the chance to work with zebrafish disease models, explore new therapeutic targets, and gain invaluable experience in experimental techniques such as CRISPR gene editing, in vivo imaging, and
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, qualifications, and experience. Dr. Sebastian Calonico in the Graduate School of Management at the University of California, Davis, wishes to employ a 100%-time Postdoc who will contribute to several projects
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involves reviewing onboarding and mentorship models from other institutions, identifying promising practices, and helping to design a phased onboarding plan tailored to the College. The RA will assist with
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-source software packages. As a Research Assistant, your primary duty will be to support the analysis of real-world data mapped to the OMOP Common Data Model, contribute to wider project planning, including
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microbes chemically/physiologically. Computational work could include genomic analyses, machine learning, and simple mathematical modeling approaches. Responsibilities Responsibilities include the following
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working group meetings; prepare agendas and take meeting notes Conduct background research on inclusion, bias reporting models, and accountability frameworks from other institutions. Support the early
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Assistantship program. If hired, you will play a key role in the CCS’s partnership with the University of Oxford on the Quill Project, a cutting-edge software platform that generates interactive digital models
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. Develops and applies numerical and statistical models to better understand and predict flood risks in coastal environments, including the integration of hydrodynamic, climatic, and socio-environmental data