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to participate in a long-term forest dynamics monitoring plot in the Republic of Palau that is part of the Smithsonian Institution’s Forest Global Earth Observatory network known as ForestGEO (https
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techniques. You will have the opportunity to participate in various projects utilizing artificial intelligence (AI) and machine learning (ML) to develop applications that optimize combat casualty care
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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
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related area, including meteorology, hydrometeorology, remote sensing, surface and atmospheric modeling, or related fields. Experience in machine learning techniques are highly desirable. Please see https
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to approximate expensive forward and adjoint simulations while preserving underlying physics. Uncertainty-aware inference: combining physics-informed learning for regularization with probabilistic generative
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missions (e.g., Surface Biology and Geology - SBG). This could involve advancing atmospheric correction, dimensionality reduction, or machine learning approaches for handling big data in order to improve
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Organization DEVCOM Army Research Laboratory Reference Code ARL-C-WMRD-300028 Description About the Research CCDC ARL Center for Agile Materials Manufacturing Science (CAMMS, https