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Machine Learning Assisted Aerosol Retrieval from Space Lidar Measurements in Planetary Boundary Layer

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Machine Learning Assisted Aerosol Retrieval from Space Lidar Measurements in Planetary Boundary Layer

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Organization

National Aeronautics and Space Administration (NASA)

Organization

National Aeronautics and Space Administration (NASA)

Reference Code

0142-NPP-NOV25-ARC-EarthSci

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 Postdoctoral Program (orau.org)

A complete application to the NASA Postdoctoral Program includes:

• Research proposal
• Three letters of recommendation
• Official doctoral transcript documents
  
  

Application Deadline

11/1/2025 6:00:59 PM Eastern Time Zone

Description

About the NASA Postdoctoral Program

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 NASAs missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology.

Description:

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, launched in April 2006 and operational until 2023, provided 17 years of valuable data. Its primary payload, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), was an elastic backscatter lidar transmitting polarized laser light at 532 and 1064 nm. CALIOP measured range-resolved backscatter intensities, enabling the detection of clouds, aerosols, and surface features. Building on CALIPSOs legacy, the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission and the planned Cloud-Aerosol Lidar for Global Observations of the Ocean-Land-Atmosphere (CALIGOLA) mission represent the next generation of lidar-based atmospheric research. EarthCARE, led by ESA and JAXA, features the Atmospheric Lidar (ATLID) operating at 355 nm. Meanwhile, CALIGOLA will deploy a three-wavelength system (355, 532, and 1064 nm) with rotational Raman channels to enhance measurement capabilities.

Traditionally, the data processing for space lidar measurements has relied on physics-based approaches. However, machine learning (ML) is increasingly being utilized in Earth science applications due to its advanced architectures, computational efficiency, and the availability of high-performance resources. ML has become invaluable for processing and analyzing large volumes of Earth observation datasets.

This opportunity focuses on developing ML-assisted retrievals of aerosols in the planetary boundary layer, emphasizing aerosol and cloud detection and classification using multichannel measurements at the highest spatial resolution. The extensive 17-year CALIOP archive provides an ideal training dataset for ML algorithms, enabling their adaptation to EarthCAREs ATLID and the future CALIGOLA mission. This research aims to generate a multi-year dataset of boundary layer aerosol properties, enhancing our understanding of aerosol-cloud interactions and their impact on climate processes.

Field of Science: Earth Science

Advisors:

Zhaoyan Liu

...@nasa.gov

(757) ###-####

Questions about this opportunity? Please email ...@orau.org

Qualifications

Applicants with a strong background in mathematics, computational methods, and machine learning, who are eager to apply their expertise to real-world challenges in Earth and atmospheric sciences, are encouraged to apply.

Point of Contact

Mikeala

Eligibility Requirements

• Citizenship: LPR or U.S. Citizen
• Degree: Doctoral Degree.

Seniority level

• Seniority level

  Internship

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Government Administration

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