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sensors, RGB/IR cameras, video systems, insect traps, and other devices to build predictive, AI- and machine-learning based models for monitoring grain quality and detecting deterioration due to mold
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quality and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how physical and chemical fiber parameters relate to dye uptake behavior, dyebath exhaustion, color
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well as exploring the application of research findings to advanced 3D models such as organoids and 3D bioprinted tissues Learning about high-content, automated phenotypic drug screening pipelines against high
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translational vaccinology within a highly collaborative research network. Within this opportunity, you will be engaged in the following learning objectives: Applying machine-learning/AI platforms including
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predictive insights that support improved food and feed safety. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: (a) utilize field and laboratory methods in plant