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scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning
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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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of tropical fruit, vegetable and ornamental crops grown in the Pacific Basin.. During this fellowship you will engage with research to extend existing computer models of surveillance traps for invasive insects
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Python Experience developing, testing, and refining machine learning models Experience developing HPC workflows Excellent written and oral communication skills Ability to function as a teammate in a
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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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the soil-water-plant-air continuum using process-based models. You will learn how to take proper soil, plant and air samples that influence carbon and nitrogen dynamics and learns how soil and plant
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, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and
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interacts with the human body—making it a key factor in both product performance and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how cotton fiber properties
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techniques. You will have the opportunity to participate in various projects utilizing artificial intelligence (AI) and machine learning (ML) to develop applications that optimize combat casualty care