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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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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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and technical gaps that prevent fundraising. Our goal is simple: Make your start-up investable by empowering participants to mature their ideas from concept to prototype to first commercial product
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skills in: High-pressure/low temperature laboratory experiments, Geomechanical characterization and analysis, High resolution image analysis Machine learning algorithm and application, Programming (C
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skills in: High-pressure/low temperature laboratory experiments, Geomechanical characterization and analysis, High resolution image analysis Machine learning algorithm and application, Programming (C
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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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packages, and help in preliminary statistical analyses. Applied Research: Help with microscopy imaging and basic molecular techniques. At the end of the internship, participants will have gained real-world
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the guidance of a mentor, the participant will be involved in the following activities: Gain expertise in advanced imaging technologies—including RGB, multispectral, and hyperspectral systems—for plant
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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