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to monitor, analyze, and predict current and evolving dynamics of complex human and natural Earth-system interactions and to deliver actionable intelligence at scales and timeframes relevant
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association studies, and interpret their results in the context of economically important traits. Evaluate and improve genomic prediction models for use in cultivar development programs. Gain experience
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machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications
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of multiple surveillance and administrative data sources. Development of reproducible analytical workflows using programming languages such as R and Python. Application of machine learning and predictive
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to model fungal population shifts and identify environmental or biological drivers of mycotoxin risk. Collaborating with plant pathologists, microbiologists, chemists, and data scientists to develop
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opportunity is currently available with the U.S. Geological Survey (USGS) located in Amherst, Massachusetts. The opportunity may also be remote. The USGS mission is to monitor, analyze, and predict current and
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functional efficacy and safety. Prediction of human health impacts is a key goal, and behavioral, physiological, and biochemical changes will be closely examined using a tiered approach to reach programmatic
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research are computational models ranging in complexity from semi-empirical aero prediction models to CFD models of varying complexity. Windtunnel and free flight experiments serve to complement and validate
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predict current and evolving dynamics of complex human and natural Earth-system interactions and to deliver actionable intelligence at scales and timeframes relevant to decision makers. As the Nation's
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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