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. Integrate deep mutational scanning data to assess viral fitness and immune escape, collaborating with experimental virologists. Work with large-scale genomic and proteomic datasets from BV-BRC, GISAID, and
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Post-Doctoral Data Science Research Fellow Oxford Centre for Diabetes Endocrinology and Metabolism (OCDEM), Churchill Hospital, Old Road, Headington, Oxford, OX3 7LE We are seeking an enthusiastic
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on collating and analyzing the large volumes of carbon cycle data gathered from the site to date, then preparing the resulting analyses for publication in scientific journals. Likely topics for papers include
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interdisciplinary study that will assess the pre-/and post-radon concentrations following deep energy-retrofits scenarios of the large-scale national renovation strategy. INFORM will utilise a building physics model
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starting June 1, 2025. Dr. Daisy Dai, Associate Dean for Research, is currently offering an NIH-funded postdoctoral position in big data analysis, substance use, neurocognition, and tobacco regulatory
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or related disciplines. They should have i) a strong quantitative background; ii) experience in analysing epidemiological data using R, STATA, SAS or other statistical packages; iii) demonstrated expertise in
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
, or a related field completed before the start date of the position Demonstrated experience with astronomical data analysis and software development Proficiency in Python programming Experience with large
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 13 days ago
, or a related field completed before the start date of the position Demonstrated experience with astronomical data analysis and software development Proficiency in Python programming Experience with large
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on advanced statistical methods for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological, and environmental factors contribute to severe mental and
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tools are need during the development of new imaging and sensing systems. With the rapid deployment of data-driven methods, repliable uncertainty quantification remains a big challenge that requires