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team designs and carries out controlled experiments and develops, validates, and applies advanced mathematical models to investigate a wide range of agricultural challenges, including the impacts
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fitness criteria of invasive and wild-type versus herbicide-resistant weed biotypes. The participant will learn to collect and analyze data related to weed control, herbicide efficacy, and comparison
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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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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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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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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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long timescales, water quantity (yield) reflects the balance between precipitation and evapotranspiration, the latter being strongly controlled by the amount, type, and growth of above-ground biomass