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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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performance on complex analytical reasoning tasks, (2) absence of assurance standards and testing protocols for AI systems processing sensitive, multi-source data, (3) opacity in delivered AI models
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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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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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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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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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-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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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