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Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and
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to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help
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computing facilities at the DOD Supercomputing Research Center. What will I be doing? This project will focus on learning, adapting, and applying US Army Corps of Engineers-developed or supported coastal
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and stress), behavior (grazing behavior halters, accelerometer-based ear tags, chute velocity measures), environmental monitoring, and pasture measures. Learning Objectives: Under the mentor's guidance
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animal health, microbiology, and aquaculture engineering. Research Project: The participant will be involved in processes to improve fish health and the efficiency of aquaculture production. Learning
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tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in precision agriculture and
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the underlying impacts of stressors on bee health and analytical skills for understanding colony level dynamics and predicting mass colony loss events. Learning Objectives: The fellow will learn and apply
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. Description The Office of Global Research (OGR), at the National Institute of Allergy and Infectious Disease (NIAID), National Institutes of Health (NIH), is seeking candidates who are interested in learning
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analysis of laboratory assay readouts, or processing and analyzing transcriptomics data (bulk or single-cell RNA-seq). Learning Objectives: Under the guidance of a mentor, the participant will have the
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animal health, microbiology, and aquaculture engineering. Research Project: The participant will be involved in processes to improve fish health and the efficiency of aquaculture production. Learning