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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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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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, software applications, record keeping, compliance training, and the principles of scientific study design. Learning both general and specialized research skills that will support advancing your scientific
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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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, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human
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, statistics, and field-lab approaches. Learning Objectives: The participant will receive training in plant molecular biology, genetics, and genomics. This research is expected to result in increased learning
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. Through these experiences, it is anticipated that you will learn how to: Operate and develop custom aerosol generation equipment including software modifications for associated chambers. Operate
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seeds. This research will help to unravel key indicators of biological relevance during seed quality testing procedures and contribute to a healthy national and international seed trade economy. Learning
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the devastating disease avian coccidiosis. The secondary goal is to compare various Eimeria spp. to identify genes involved in intestinal cell specificity, virulence, and markers of drug resistance. Learning
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in each crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide