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promoters. Digital Phenotyping: Application of hyperspectral imaging and advanced imaging tools to detect disease traits beyond the visible spectrum. AI-Driven Data Analysis: Leveraging machine learning
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process, contact NETLinfo@orau.org . After you have submitted an application in Zintellect, you may reach out to internship.program@netl.doe.gov to request to talk with the hosting researcher if you would
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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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in learning how the scientific process is used to solve agricultural problems caused by insect pests. Our respective research programs are focused on using cutting-edge techniques to better understand
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in preharvest and post-harvest production and processing. This project will focus primarily on safety and quality inspection using spectral imaging techniques such as fluorescence, reflectance, and
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-automated processing pipeline capable of analyzing high-throughput plant phenotyping and soil-sensing data to extract key phenotypic traits. Advancing crop productivity within sustainable cropping systems
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this fellowship, you will participate in research projects involving canine biometric data, looking for novel ways to identify and individuate dogs using neural networks, local feature mapping, image classifiers
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management of field plot trials, data collection, and database management. Experience in large data analyses Experience with operation optimization Experience with machine learning, image analysis Experience
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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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machine learning, image recognition, and prediction of damage to tree nuts from insect pests. They will also collaborate with other team members on statistical analysis of data collected as part of