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improving plant health using machine learning and artificial intelligence. Mentor(s): The mentor for this opportunity is Yulin Jia (yulin.jia@usda.gov ). If you have questions about the nature of the research
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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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-Docs, post-Bacs, summer internships, etc.) to those interested in research in the following fields: Theory and application of machine learning and artificial intelligence including Natural
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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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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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online databases or interactive websites. Learning Objectives: TUnder the guidance of a mentor, the participant will learn techniques in genomic epidemiology and machine learning to quantify drivers of IAV
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spectroradiometers. Ability to apply AI tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in
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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
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the use of workflow tools, development environments, and resources to contribute to and implement shared bioinformatic workflows. Experiences may extend into training on Machine Learning and AI models as