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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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rainfall–runoff models, such as HEC-HMS and the National Water Model, to support more reliable flood frequency analysis and improve flash flood predictions in small watersheds. Under the guidance of a mentor
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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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) to predict crop yields, nutrient uptake and losses; perform model calibration/validation as needed. In addition, you will have an opportunity to use well established crop/farm decision support tools
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performance. Through this experience, you will gain hands-on exposure to analytical instrumentation, quantitative analysis, and modern predictive modeling techniques. Learning Objectives: Under the guidance
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of unknown PFAS, supporting agricultural research and advancing the ARS mission. The project will involve compiling high-resolution mass spectrometry (HRMS) databases for PFAS, creating machine learning models
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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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transport, deposition, application system performance, and the measurement and modeling technologies that support these systems. Research Project: Under the guidance of a mentor, you will have the opportunity
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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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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