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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 to
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aircraft (Cloud Physics Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and
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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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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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, 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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atmospheric modeling, or related fields. Experience in machine learning techniques are highly desirable. Please see https://science.gsfc.nasa.gov/earth/mesoscale for more information about the
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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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States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S. Lawful Permanent Residents (LPR
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Education (ORISE) participant, you will join a community of scientists and researchers to conduct research related to the electric propulsion of transport aircraft. Your research activities will be designed
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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://www.arl.army.mil