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limited to): Implement machine learning transfer learning methods to homogenize AOD observations from the geostationary constellation of weather and atmospheric monitoring satellites (e.g. GEOS
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project include (but are not limited to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine
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, high-gain antenna systems, high-efficiency transmitters, low-phase noise RF sources, and other critical radar components. Topics also cover radar signal processing and machine learning, applying
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, or machine learning approaches for handling big data in order to improve scientific insight and actionable information derived from NASA datasets. Candidates interested in leveraging multi-mission
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experience with remote sensing data, specifically LiDAR (e.g., GEDI), Synthetic Aperture Radar (SAR), and multispectral optical time-series. Experience exploring machine learning and deep learning