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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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. Under the direction of cognizant Federal personnel, participating researchers have opportunities to learn how to lead tasks, explore new research areas for DHS, and participate in networking opportunities
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. Under the direction of cognizant Federal personnel, participating researchers have opportunities to learn how to lead tasks, explore new research areas for DHS, and participate in networking opportunities
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decision support data or analytical capabilities. * Generating computationally numerical algorithms for data processing and analysis, using supervised and unsupervised machine learning models and methods
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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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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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aided design (CAD), computer aided manufacturing (CAM), manipulation of digital manufacturing software tools, 3D object slicers, support structure optimizers, computer programmings, and scripting
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should I apply? As the selected candidate and under the guidance of a mentor, you will learn how research advances solutions to increase resilience, recovery, and readiness to maximize service member
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on the extent and use of the Nation’s transportation system, how well the system performs, and the effects of the system on society and the environment. Are you ready to learn how to build consensus around
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, high-efficiency transmitters, low-phase noise RF sources, and other critical radar components. Topics also cover radar signal processing and machine learning, applying advanced techniques to enhance