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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 22 hours ago
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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website (http://www.mouncelab.com). Experience in molecular virology is preferred but not required. Responsibilities are flexible according to discussions between the applicant and the PI.Interested
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 22 hours ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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Certificates/Credentials/Licenses N/A Computer Skills MS Office and willingness to learn software used in the research lab Supervisory Responsibilities No Required operation of university owned vehicles No Does
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will continue to build from our learnings. https://pubs.rsc.org/en/content/articlelanding/2025/gc/d5gc01813g https://pubs.rsc.org/en/content/articlehtml/2018/gc/c7gc03747c https://pubs.rsc.org/en/content
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. Proficiency in programming languages for data analysis (e.g., Python, R) and experience with machine learning, statistical modeling, and wearable sensor data analysis is desirable. We expect you to be able
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veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL | Opportunities for Veterans at Brookhaven National
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of traumatic extremity injuries and amputations with a specific focus on translating their findings into clinical practice to improve the care of injured Service Members and Veterans. To learn more, visit: https
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 22 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and