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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 8 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 8 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 8 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