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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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biological and biogeochemical constituent concentrations and rates from the derived optical properties. Specifically, the aims are to develop advanced algorithms to retrieve from PACE and GLIMR remote sensing
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Organization U.S. Department of Agriculture (USDA) Reference Code USDA-ARS-SEA-2026-0267 How to Apply To submit your application, scroll to the bottom of this opportunity and click APPLY. A complete application consists of: An application Transcript(s) – For this opportunity, an unofficial...
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. Description: NASA Inexpensive Network Sensor Technology for Exploring Pollution (INSTEP) is a low-cost air quality sensor network utilized in combination with NASA remote sensing datasets to analyze air quality
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ecology, remote sensing, and computer science. UAV imagery and increasingly sophisticated and targeted AI algorithms can estimate forage quality, biodiversity, and the abundance of individual plant species
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and soil assessment, hydrologic monitoring, remote sensing, GIS applications, and processed-based model calibration. The program is designed to develop you into an independent researcher capable
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center on aspects of bee biology as they relate to heat, hypoxia, and reduced ambient air pressure. Further training will involve sophisticated approaches to remote sensing data acquisition, analytical
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leadership in agricultural discoveries through scientific excellence. Research Project: USDA-ARS in Manhattan, KS is developing affordable remote-sensing tools to rapidly detect insect activity and grain
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stratigraphy, weak layer formation, and avalanche initiation. Enhancing spatial and temporal snowpack datasets through field measurements, automated instrumentation, and remote sensing. Collaborate
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of the research project, research may also incorporate advanced optical diagnostics, quantitative image analysis, computational modeling, and remote sensing technologies to improve understanding, evaluation, and