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contamination in corn remains one of the most persistent threats to U.S. agriculture, with significant implications for food safety and crop quality. Current satellite imaging technologies lack ability to detect
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of incorporating sensors, spectroscopy, imaging, and machine learning techniques into the postharvest processing workflows and/or pre-harvest evaluation of food quality and safety. The participant will have the
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be in English or include an official English translation. If you have questions about the application process, contact NETLinfo@orau.org . After you have submitted an application in Zintellect, you may
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will I be doing? Under the guidance of a mentor, you will gain knowledge in various applications of AI techniques to geospatial problems at the basic research level, including image analysis, synthesis
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Language Processing / Large Language Models, image classification and object detection and Aided/Automated Target Recognition or other applications of interest to the Army to improve warfighting, lethality, and
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using sensors, data loggers, and thermal images Lab experiments examining how nutrition affects performance and physiological traits Field experiments quantifying behavior of insects Mentor(s): The mentor
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data have the potential to rapidly check animals and serve as a tool for farmers, ranchers, and researchers. Current research is being done by PI to automate imaging and spectral data to develop and
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tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in precision agriculture and
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and maintain equipment for measuring gas exchange, image and spectral data collection and analysis, and the effect of different turfgrass management options on its visual quality, sustainability and
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magnetograms and EUV images. A key component of the analysis will be measuring plasma composition (elemental abundances), as these abundances are believed to remain largely unchanged from the corona