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will include examining fiber properties such as crystallinity, pore size distribution, fiber surface content, and metal ion composition, as well as conducting laboratory-scale dye uptake trials with
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, implementation of management treatments, and the use of coding and process-based models to analyze natural resource dynamics. Learning Objectives: Under the guidance of a mentor, you will build experience in plant
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protein expression and purification, biochemical screening assay development, computational structural modeling, cell-based antiviral validation studies, and exposure to preclinical small-animal research
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personal professional network with federal, university, and industry scientists through collaboration and stakeholder engagement. Mentor(s): The mentor for this opportunity is Nicholas LeBlanc
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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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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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to model fungal population shifts and identify environmental or biological drivers of mycotoxin risk. Collaborating with plant pathologists, microbiologists, chemists, and data scientists to develop