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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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, 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 (ML), to help solve complex agricultural
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analyses. Learning Objectives: By collaborating closely with members of our interdisciplinary research team, you will gain invaluable hands-on experience to all phases of the research process. You will
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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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Experience or interest in artificial intelligence (AI), machine learning, or AI-assisted workflow automation Experience with software testing, beta testing, user acceptance testing, or information system
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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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submitted. If email is not received, check the junk folder before contacting ORISE to confirm submission. To apply and learn more about EIF and host institutions – such as eligibility, requirements, and
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of multiple surveillance and administrative data sources. Development of reproducible analytical workflows using programming languages such as R and Python. Application of machine learning and predictive
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. This fellowship requires in-person participation in Manhattan, Kansas. Learning objectives: During this appointment, you will have the opportunity to: Gain experience in sorting and identifying insects of medical
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part of everyday life. As an Oak Ridge Institute for Science and Education (ORISE) participant, you will join and learn from a community of scientists and researchers engaged in planning and