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to better understand and characterize variability of water at the land surface - i.e. in soils, snow and groundwater - to help in predictions of future water availability, global water cycle dynamics and sea
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of economically important traits. Evaluate and improve genomic prediction models for use in livestock selection programs. Integrate genomic data into applied breeding strategies to accelerate genetic improvement in
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national scale programmatic decisions and policy. Specific activities include: Operationalize, and where needed, improve Bayesian spatio-temporal feral swine abundance model allowing predictions to be
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drugs and proteins, biosimilar proteins, generic peptides, and nanoparticle therapeutics. Project will apply predictive analytical tools to identify patient populations at risk for adverse immune
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include the development of predictive models for disease resistance, genome-wide association studies to uncover resistance loci, automated phenotyping approaches using image data, and integrative multi
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integration and technology selection. Participating in aircraft operations trade studies to evaluate economic viability and military effectiveness. Developing and applying analytical skills to predict installed
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and disentangle interacting stressors, and develop a proactive framework to predict and help prevent future mass colony loss events. The research fellow will develop laboratory tools for characterizing
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on increasing variability and extreme events in watersheds. Expand knowledge of process-based modeling approaches to assess relationships between forest species composition, biomass, and water yield Develop