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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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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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the safety of medications used during lactation. Learning Objectives: Under the guidance of the mentor, you will receive structured, hands-on training in MPS design, operation, and analytical evaluation
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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