8 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" positions at National Aeronautics and Space Administration (NASA)
Sort by
Refine Your Search
-
Listed
-
Field
-
National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 hour ago
and machine learning, and for the public to see the worlds of the outer solar as they would appear to our eyes for the first time. The envisaged project includes: image selection, cleaning and
-
National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 28 minutes 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
-
National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 29 minutes 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
-
National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 30 minutes ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
-
National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 32 minutes ago
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); Foreign Nationals eligible for an Exchange Visitor
-
National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 32 minutes ago
of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 34 minutes ago
that are facile with computationally efficient, rigorous machine learning for image region identification, demonstrate an understanding of both planetary and scalable computer science, and have publication
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
). Revisiting Ionosphere-Thermosphere Responses to Solar Wind Driving in Superstorms of November 2003 and 2004. J. Geophys. Res., 122. https://doi.org/10.1002/2017JA024542 . 2. McGranaghan, R. M., A. J. Mannucci