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are not limited to, Developing new computational methods and analytical tools, with particular emphasis on machine learning and artificial intelligence approaches. Identifying signatures of viral adaptation
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-Docs, post-Bacs, summer internships, etc.) to those interested in research in the following fields: Theory and application of machine learning and artificial intelligence including Natural
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improving plant health using machine learning and artificial intelligence. Mentor(s): The mentor for this opportunity is Yulin Jia (yulin.jia@usda.gov ). If you have questions about the nature of the research
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humans and intelligent agents to work together cooperatively to achieve desired outcomes most efficiently. This research will focus on understanding how to leverage human inputs to increase
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. The overall objectives of the project include: Identify mathematical relationship among normalized difference vegetation index, plant developmental stages and plant health. Develop artificial intelligence
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of the relevant fields (Biology, Biochemistry, Cellular and Molecular Biology, Neuroscience, Veterinary Science, Veterinary Microbiology, Artificial Intelligence, or related field). Degree must have been received
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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 artificial intelligence (AI) and machine learning, to help
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The qualified candidate will have a bachelor's degree in entomology, microbiology, artificial intelligence, scientific computing, or other eligible discipline. Degree must have been received within 24 months
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Participation Program at the U.S. Department of Defense . Qualifications The ideal candidate will have a doctoral degree in Geospatial Engineering, Geographic Information Systems, Artificial Intelligence
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advanced methods to generate insight and reduce burden (Analytics and Artificial Intelligence). Creating trusted, ready-to-use data (Data Management). Equipping staff to work independently with data (Data