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cycles. Understanding these complex interactions requires measurements that go beyond traditional multi-spectral imaging. Imaging spectroscopy—capturing the full contiguous spectrum from the Visible
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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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-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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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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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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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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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
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about the application process, please email ORISE.ARS.Midwest@orau.org and include the reference code for this opportunity. Qualifications The qualified candidate should be currently pursuing or have
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-liquid interface in vitro models of the human lung Gaining experience preparing routine chemical and drug solutions Conducting in-depth analysis of high-throughput data Interpreting fluorescent image
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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