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will support genome-wide prediction of variant effects across pathogen populations represented in USDA-ARS culture collections. Simultaneously, protein language models and structural methods will
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. During this training opportunity, you will have the opportunity to be involved in the development of a training model to predict PRRSV disease outcomes in field-collected samples from pigs infected with
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predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications, and mortality. Gain experience analyzing administrative
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scientists to characterize movement patterns of stored-product insects across U.S. agroecosystems and to identify invasion pathways. This includes modeling environmental variables that predict population
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to keep the digital twin synchronized with the physical state in near real-time Connecting predictive ML models, computer vision systems, and simulations with visualization platforms to provide actionable
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Organization Centers for Disease Control and Prevention (CDC) Reference Code CDC-2026-0054 How to Apply To submit your application, scroll to the bottom of this opportunity and click APPLY. A
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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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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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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