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Field
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 17 days ago
of ozone. Of particular interest is the challenging upper troposphere and lower stratosphere (UTLS) where ozone can impact air quality and weather on a range of spatial and temporal scales. The successful
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Strong statistical/econometric skills with demonstrated experience working with Stata, R or spatial mapping software Some knowledge of seed systems, crop science and/or crop modelling is desirable
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methods. Familiarity with spatial analysis and modeling techniques (e.g., GIS, spatial statistics, population modeling). Excellent communication skills, both written and oral. Ability to work independently
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technologies and drone-based measurement platforms are creating new opportunities to study the composition of the atmosphere at unprecedented spatial, temporal and vertical scales. In this position, you will
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Position Details Position Information Internal Posting? Posting Number SP004690P Position Title Postdoctoral Research Associates, Statistical and Computational Modeling Division/College University
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the impact of polyamines on macrophage phagocytosis and efferocytosis activity; 3) in vivo experiments testing therapeutic efficacy of polyamines; and 4) transcriptomic analyses (scRNA-seq and spatial) across
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research for an NIH-NSF funded project on human health, movement, and infectious diseases. Successful applicants will have experience in: Either spatial analysis using GIS or disease modeling in R and/or
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surface temperature) with eddy flux measurements to scale up findings and develop predictive models of water use efficiency, and c) quantify water use efficiency and its temporal and spatial variability
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quantitative ecology, applied statistics, or a related field with strong background in statistics and model development. Experience with R and analyzing spatial datasets. Ability to apply quantitative methods
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 21 hours ago
to identify strategies for maximizing desired subsurface outcomes (e.g., recovery efficiency, increased storage) Apply geostatistical methods (e.g., kriging) to characterize the spatial heterogeneity of key