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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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sensors, RGB/IR cameras, video systems, insect traps, and other devices to build predictive, AI- and machine-learning based models for monitoring grain quality and detecting deterioration due to mold
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models with Artificial Intelligence (AI) methods to enhance model interpretability, accuracy, and predictive power. You will also have opportunities to contribute to multidisciplinary, multi-institution
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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
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use of a variety of computer models for the prediction and planning of test event The selected candidate must be able to pass interim SECRET security clearance requirement, hold a SECRET security
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, Montana. The USGS mission is 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