15 machine-learning Postdoctoral positions at National Aeronautics and Space Administration (NASA) in UNITED-STATES

ICAR - Advancing Multi-Messenger Biosignature Techniques with Machine Learning

National Aeronautics and Space Administration (NASA) | United States | about 17 hours ago

. Description: The Interdisciplinary Consortia for Astrobiology Research (ICAR) project on Advancing Multi-Messenger Biosignature Techniques using Machine Learning seeks suitable applicants to work on any of a

Machine Learning Applications in Solar System Exploration

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 17 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

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

Organization National Aeronautics and Space Administration (NASA) Reference Code 0202-NPP-MAR26-JPL-HelioSci How to Apply All applications must be submitted in Zintellect Please visit the NASA Postdoctoral Program website for application instructions and requirements: How to Apply | 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 17 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

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

Imaging the Outer Planets in the Voyager Grand Tour

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 17 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 4 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

Physical and AI Based Satellite Remote Sensing Algorithm-development and Applications

National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | about 17 hours ago

atmospheric science, physics, and computer science. Experience in analyzing satellite data and geospatial data are desired. Skills in AI, machine learning, deep learning, keras, Pytorch are preferred. A proven

A Next Generation Quick Fire Emissions Dataset (QFED)

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