472 programming-"Multiple"-"Duke-University"-"O.P"-"FEMTO-ST-institute" "U.S" Fellowship positions in United Kingdom
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. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous. The ability to work
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be doing? The appointed researcher will be part of the Academic Centre for Mental Health and lead a programme of work focussed on analysing routine data and data collected by the Centre for Pesticide
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including malaria, have experience and understanding in using multiple metrics for analysis, experience with design and implementation of study protocols, experience of data analysis in Stata and programming
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resources, and plan and progress work activities on multiple projects Desirable Interview Further Information Grade Clinical Academic Trainees Salary £52,656 - £73,992 Work arrangement Full-time Duration 30
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researcher will be part of the Academic Centre for Mental Health and lead a programme of work focussed on analysing routine data and data collected by the Centre for Pesticide Suicide Prevention (CPSP
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About the Project We are seeking a talented and dedicated team of scientists, bioinformaticians and support colleaguesto join the ground-breaking PharosAI initiative – a £43.6M national programme co
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Development Lead. You will be responsible for contributions to a major new programme of research investigating ethical questions and public views concerning research using post-mortem brains. The project
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About the Project We are seeking a talented and dedicated team of scientists, bioinformaticians and support colleagues to join the ground-breaking PharosAI initiative – a £43.6M national programme
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Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
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partners. Main Duties Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species