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emerging technologies, transportation data, policy, research and all modes of transportation across the Department. As a Fellow, you will learn to facilitate the transformation of our transportation system
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collaboration and stewardship of ARS's research data and other knowledge assets. PDI is the only in-house public-facing enterprise-level data solutions provider for ARS scientists. Learning Objectives: 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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artificial intelligence and machine learning, will be woven through each of these three areas. Benefits to you as a SMaRT Intern: The interdisciplinary atmosphere provided at UT-ORII will expose you to team
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comfortable observing, handling, and caring for plants and insects, while also setting up eavesdropping devices and analyzing data derived from the instrumentation. Learning outcomes will center on plant
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anticipated to receive by July 2027. Preferred skills: Academic training in computer science, artificial intelligence or machine learning, data science, bioinformatics, computational biology, epidemiology
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crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide you with
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development, and evaluation, by learning to implement automation, integrating data across systems (SharePoint, Teams, Salesforce, and 1CDP (Palantir System), and creating operational dashboards under
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learn best practices in instructional design and health communication, including web communication, application of CDC’s quality training standards, user experience, digital content design, and plain
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,” an autonomous environmental robot outfitted with GPS, a downward-facing camera to capture images of surficial sediment, and an onboard machine learning model for image processing. SandHound will be capable