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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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and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used
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experience with time-series data analysis and machine learning including reinforcement learning. Applicants should be proficient in Matlab and/or Python Point of Contact ARL-RAP Eligibility Requirements
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also explore data integration techniques, apply machine learning approaches, and utilize high-performance computing resources to contribute to achieving the project objectives. Learning Objectives: With
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Ranger district in partnership with the local government and community organizations) contribute to local economies. Learning Objectives: The fellow will have opportunities to strengthen their research
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tephritid fruit flies, which are important horticultural pests globally. The successful candidate will participate in the design, execution, and analysis of insect behavioral studies using HR, learning and
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for improving airfields, communication systems, ammunition storage facilities, and other locations. In addition, you will learn about web visualization efforts that impact the design and construction mission
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gain learning experience in developing optical fiber gas sensors, synthesizing and testing various gas sensing materials, testing and optimizing sensor performance for applications in gas sensing
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to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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on-site in Oak Ridge, Tennessee. $500 per week stipend based on full-time of participation each week. The opportunity to learn from world-renowned scientists and engineers. Optional professional development