11 postdoctoral-machine-learning "https:" Postdoctoral research jobs at National Aeronautics and Space Administration (NASA)

Next-generation wildfire risk assessment using deep learning based high resolution fuel maps and stochastic fire spread modeling

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago

should have a strong background in data science, machine learning, remote sensing, wildfire science, or a related field. Experience with deep learning frameworks, satellite observations (e.g., Sentinel

Machine Learning Applications in Solar System Exploration

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 6 hours ago

and related activities. Strong programming skills are a must, including experience with machine learning. Good written and oral communication skills in English are important. Point of Contact Mikeala

Imaging the Outer Planets in the Voyager Grand Tour

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 6 hours 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

Advancing Terrestrial Ecology, Wildfire Fuel Monitoring, or Agricultural Applications, or Associated Activities through VSWIR Imaging Spectroscopy

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago

missions (e.g., Surface Biology and Geology - SBG). This could involve advancing atmospheric correction, dimensionality reduction, or machine learning approaches for handling big data in order to improve

Advanced Aerosol Data Assimilation of the GEO-LEO Constellation

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month 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

Aerosol and Cloud Remote Sensing using Lidar

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month 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

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

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month 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

Remote sensing of snow parameters in support of Surface Biology and Geology (SBG) mission

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 1 month ago

Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal

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

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 month ago

Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal

Assimilation of Hyperspectral Infrared Radiance Observations in the GEOS

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month 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