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Machine Learning Assisted Aerosol Retrieval from Space Lidar Meas...
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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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ORAU Machine Learning Assisted Aerosol Retrieval from Space Lidar Measurements in Planetary Boundary Layer
1 month ago Be among the first 25 applicants Join to apply for the
Machine Learning Assisted Aerosol Retrieval from Space Lidar Measurements in Planetary Boundary Layer
role at
ORAU Get AI-powered advice on this job and more exclusive features. 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 NASA’s 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 CALIPSO’s 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 EarthCARE’s 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
zhaoyan.liu@nasa.gov
(757) 256-5706
Questions about this opportunity?
Please email npp@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 Referrals increase your chances of interviewing at ORAU by 2x Sign in to set job alerts for “Machine Learning Specialist” roles.
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Join to apply for the
Machine Learning Assisted Aerosol Retrieval from Space Lidar Measurements in Planetary Boundary Layer
role at
ORAU Machine Learning Assisted Aerosol Retrieval from Space Lidar Measurements in Planetary Boundary Layer
1 month ago Be among the first 25 applicants Join to apply for the
Machine Learning Assisted Aerosol Retrieval from Space Lidar Measurements in Planetary Boundary Layer
role at
ORAU Get AI-powered advice on this job and more exclusive features. 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 NASA’s 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 CALIPSO’s 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 EarthCARE’s 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
zhaoyan.liu@nasa.gov
(757) 256-5706
Questions about this opportunity?
Please email npp@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 Referrals increase your chances of interviewing at ORAU by 2x Sign in to set job alerts for “Machine Learning Specialist” roles.
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