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operations, senior scientists within the IC annually pinpoint vital research topics spanning diverse disciplines, from artificial intelligence, quantum computing and sensing, biotechnology, and energy and
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and weaknesses for end-users. Help develop new or improve existing soil moisture estimates using NISAR and other datasets utilizing artificial intelligence (AI) and machine learning. The outcome from
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the guidance of a mentor, this opportunity will involve: developing and applying methods in computational biology and artificial intelligence to gather information about gene function in the legume family; using
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phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring
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interfaces supporting the CAMP staff with the development and implementation of reports required at multiple levels of leadership (clinic, facility, enterprise). Leveraging artificial intelligence to optimize
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Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and
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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