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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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, austere conditions. Learning about military deployment health and gain experience in environmental data collection. Contributing to solutions for difficult environmental health problems in complex
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research in several areas. These include, but are not limited to: Exploring machine learning techniques to analyze current systems and assess opportunities for improvement Gaining experience with virtual
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resolution visualizations of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter
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of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large
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in genetically modified maize hybrids. Outcomes will contribute long-term goals to develop tools to detect and monitor resistant insects in field populations. Learning Objectives: Participants will
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learn how phenotypic datasets are integrated with genomic data for association analyses, genomic selection, and AI-driven methods, including machine learning and deep learning, to enhance germplasm
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science and technology to reduce risks and costs within the EM regulatory framework. The Scholar will participate in learning and development opportunities, be actively mentored by EM staff, as
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machine learning, image recognition, and prediction of damage to tree nuts from insect pests. They will also collaborate with other team members on statistical analysis of data collected as part of
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ecosystem services that they provide. Learning Objectives: The participant will learn to utilize ecological simulation models and to design and conduct geospatial analysis of model results to characterize