Integrating Satellite, Surface, and Model Data into Machine Learning Algorithms

Organization

National Aeronautics and Space Administration (NASA)

0269-NPP-NOV
26-GSFC-Earth Sci

11/1/2026 6:00:59 PM Eastern Time Zone

The NASA Postdoctoral Program (NPP) offers unique research opportunities to highly‑talented scientists to engage in ongoing NASA research projects at a NASA Center, NASA Headquarters, or at a NASA‑affiliated research institute. These one‑to‑three‑year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology.

This opportunity is closed to applicants who are Senior Fellows (5‑years or more past PhD).

NASA and other agencies worldwide have been observing atmospheric aerosols from space and ground platforms for more than three decades. In addition, there are model outputs in the form of forecasts and reanalysis. There is a suite of aerosol retrievals from sensors such as MODIS, VIIRS, OMI, MISR, ABIs, AHI, and similarly ground measurement records such as Air Now, AERONET, and others.

The climate and air quality research and operational community worldwide have extensively used these datasets independently or in combination. The use of satellite observations for air quality monitoring is emerging and advancing quickly. The global air quality community is moving towards a hybrid system where regulatory, low‑cost sensors and satellite observations play their role. Thus, there is a huge demand for integrated datasets to support traditional research and application and the modern cloud and machine learning community.

In this project, we intend to develop tools using commercial cloud computing services to accelerate the use of earth observations in particulate matter air quality research and applications to enable science in the cloud. To facilitate this objective, we will generate invaluable high‑quality training and validation data sets labeled and ready to use in machine learning algorithms that will benefit current and future satellite missions (TEMPO, MAIA).

This project will take a fleet of satellites, ground‑based, and model output, fuse them at appropriate spatial and temporal scales and enable science and decision making in the cloud using new machine learning techniques. This effort will develop science on the cloud and improve and expand the use of NASA satellite data to a broader community.

Earth Science

Pawan Gupta

Email: pawan.gupta

Phone:

• U.S. Citizens
• U.S. Lawful Permanent Residents (LPR)
• Foreign Nationals eligible for an Exchange Visitor J‑1 visa status
• Applicants for LPR, asylees, or refugees in the U.S. at the time of application with
  1) a valid EAD card and
  2) I‑485 or I‑589 forms in pending status
• Degree:
  Doctoral Degree.