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have an interest in substance use, posttraumatic and/or operational stress, suicidality, and artificial intelligence/machine learning (AI/ML). What will I be doing and why should I apply? As the selected
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-breeding innovation through artificial intelligence and data-driven approaches. You will help develop machine-learning tools that enhance decision-making for weed management across the U.S. Cornbelt and key
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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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. This opportunity is designed to enhance your learning and professional development through engagement in research activities such as evaluating customer feedback approaches, analyzing organizational processes, and
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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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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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health to improve the care and outcomes for mothers and babies, intervention and prevention activities. Training will occur under the direction of an assigned mentor. Learning Objectives: The purpose
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be integrated to enhance phenotyping and support research and development efforts. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: Detail how genetic and
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experience learning how surveillance and research projects are conducted, including case-control studies designed to identify risk factors for birth defects and/or stillbirth. Learn to translate scientific
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improvement. Through guided learning experiences, you will develop skills to manage and analyze program datasets, evaluate partnership activities, and produce actionable recommendations that enhance program