42 big-data-and-machine-learning-phd-"https:" Postdoctoral positions at National Aeronautics and Space Administration (NASA)

Characterization of ionospheric dynamics through application of machine learning techniques to GPS measurements

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 3 months ago

, seasonal and solar cycle dependent variabilities. These different phenomena make characterization of regional ionospheric dynamics a complex problem. New observational capabilities and sophisticated data

Advanced Aerosol Data Assimilation of the GEO-LEO Constellation

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 hours ago

assimilation Integration (JEDI) framework for atmospheric data assimilation Develop machine learning foundation models based on simulated observations from high fidelity atmospheric simulations for developing

Data Assimilation of Combined Lidar and Polarimeter Observations for the AOS Mission

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 hours 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

Cosmic Dawn and Large-scale Structure with Intensity Mapping

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 months 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

Advanced Aerosol Data Assimilation of the GEO-LEO Constellation

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago

assimilation Integration (JEDI) framework for atmospheric data assimilation Develop machine learning foundation models based on simulated observations from high fidelity atmospheric simulations for developing

Data Assimilation of Combined Lidar and Polarimeter Observations for the AOS Mission

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months 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

Strengthening NASA Air Quality Observations with Low-Cost Sensor Data

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 months ago

analysis software packages. Demonstrated success in collaborative environments. Experience with one or several of the following: machine learning, data assimilation, ArcGIS, NASA satellite datasets. Point

A Next Generation Quick Fire Emissions Dataset (QFED)

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 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

Assimilation of Hyperspectral Infrared Radiance Observations in the GEOS

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months 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

Stratospheric Composition in a Changing Climate

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago

scholar to conduct in-depth analysis of stratospheric data, using data from the Microwave Limb Sounder (MLS) as well as complementary datasets like the Atmospheric Chemistry Experiment Fourier Transform