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are not limited to, Developing new computational methods and analytical tools, with particular emphasis on machine learning and artificial intelligence approaches. Identifying signatures of viral adaptation
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for wheat, barley, oat, and rye. As part of a highly collaborative, multi-disciplinary team, the selected candidate will use his/her computational biology and machine learning background to help develop tools
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about the most recent advances in machine learning and data management in agricultural research. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on using machine
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-Resistant Organism Repository and Surveillance Network (MRSN) is a unique entity that serves as the primary surveillance organization for antibiotic-resistant bacteria across the Military Health System (MHS
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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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statistical software. Learning Objectives: Learn about the implementation of the application of machine learning methodologies in plant phenotyping and genotyping for the sugarcane molecular biology lab. Learn
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Agrilus pest in Northern U.S.. Learning Objectives: Through participation in the research project, the participant will learn principles of classical biological control as well critical thinking skills and
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to a motivated postdoctoral fellow interested in learning and using contemporary functional genomics approaches (CRISPR, RNAi, transgenics, etc.) to characterize the functions of genes involved in
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the processing capability for both research within ARS and the stakeholders in Hawaii. The participant will get to explore the possibility of incorporating sensors, spectroscopy, imaging, and machine learning
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findings will be encouraged and supported. Learning Objectives: The fellow will have the opportunity to gain or expand skillsets over a range of computational techniques needed for modern agricultural