NASA GESTAR II Research Opportunity: Atmospheric Aerosols, Satellite Data, and Air Quality Forecasting at GSFC
The Goddard Earth Sciences Technology and Research II (GESTAR II) program, administered by the University of Maryland, Baltimore County (UMBC), is currently offering an advanced research position in atmospheric aerosols and satellite-based air quality forecasting. Hosted at NASA Goddard Space Flight Center (GSFC), this role is designed for candidates with specialized expertise in atmospheric science, remote sensing, and machine learning. It represents a significant opportunity for early-career and mid-career researchers aiming to contribute to high-impact environmental monitoring and predictive modeling projects.
Join NASA’s cutting-edge air quality research at GSFC through the GESTAR II program. Work with satellite data, machine learning, and aerosol modeling to forecast pollution events and support environmental policy. This is a rare opportunity to contribute to global climate science and public health from a premier research institution.
Program Overview
Program Name
Research Position: Atmospheric Aerosols, Satellite Data, Air Quality Monitoring and Forecasting – NASA GESTAR II at GSFC
Host Country / Region
United States / NASA Goddard Space Flight Center, Maryland
Funded By
University of Maryland, Baltimore County (UMBC) in collaboration with NASA GSFC
Duration
Full-time position; duration is open-ended based on project performance and funding availability.
Study Mode
On-site (NASA GSFC
Key Responsibilities
This research role is focused on leveraging satellite observations and machine learning methodologies to improve air quality forecasting models. The appointed candidate will:
Develop and enhance aerosol remote sensing techniques using multi-sensor satellite data, including MODIS, VIIRS, TROPOMI, and CALIPSO.
Integrate advanced machine learning algorithms with atmospheric modeling tools to improve the accuracy of pollution forecasts.
Perform comparative assessments between remote sensing outputs and in-situ ground-based measurements.
Contribute to the validation and refinement of chemical transport models (e.g., GEOS-Chem, NAAPS, CMAQ).
Support the transition of research algorithms into operational forecasting systems.
This work directly contributes to NASA’s strategic goals of improving Earth system understanding and supporting public health and environmental policy through robust air quality intelligence.
Eligibility Criteria
To be considered for the position, applicants must meet the following qualifications:
Hold a Ph.D. in atmospheric sciences, earth sciences, environmental science, remote sensing, or a closely related field.
Demonstrate proven research experience in aerosol retrieval or air quality modeling, with a strong peer-reviewed publication record.
Proficient in handling large satellite datasets and programming in Python, MATLAB, R, or IDL.
Able to work independently
and collaboratively in a research-intensive environment.Committed to open science practices and interdisciplinary collaboration.
Application Process
Submission Instructions
Interested candidates should submit the following materials via email:
Cover Letter
Curriculum Vitae (CV)
Complete List of Publications
Three-Page Research Statement outlining current expertise, future goals, and alignment with NASA GESTAR II objectives
Email Address
Applications must be sent to [email protected] with the email subject line: “Task 173: Researcher Position”
Recommendation Letters
Three letters of recommendation will be requested for shortlisted candidates. Referees should be prepared to comment on the applicant’s research capabilities, work ethic, and collaborative potential.
Deadline
Primary Review Deadline: July 18, 2025
Extended Consideration: Applications will be accepted on a rolling basis until the position is filled
Areas of Research Specialization
The researcher will work across the following domains:
Satellite Remote Sensing
Utilize data from MODIS, VIIRS, MISR, and other satellite instruments to monitor aerosol properties such as optical depth, type, and distribution.
Machine Learning in Earth Sciences
Apply deep learning models, regression techniques, and data assimilation to enhance predictive capability in real-time air quality forecasting.
Air Quality
Modeling
Evaluate and integrate atmospheric composition data into chemical transport models. Assess model performance through comparisons with ground-based sensors and airborne instruments.
Research-to-Operations Transition
Support NASA’s efforts to move cutting-edge research tools into practical operational use for governmental and public health stakeholders.
Tools and Technical Skills Required
Applicants are encouraged to demonstrate proficiency in the following:
Aerosol Optical Depth (AOD) retrieval algorithms
Statistical modeling and uncertainty quantification
Experience with GRASP or similar aerosol retrieval frameworks
Familiarity with global and regional atmospheric transport models
Cloud-based or high-performance computing environments (HPC, AWS, NASA’s Discover)
Institutional Context
GESTAR II is a robust research consortium that supports over 120 scientists embedded within NASA GSFC. Researchers benefit from access to cutting-edge facilities, mentorship from NASA scientists, and the opportunity to contribute to high-priority NASA missions related to climate, weather, and atmospheric composition.
Learn more about the GESTAR II framework and research culture at https://gestar2.umbc.edu.
Career Development and Impact
This role offers a unique opportunity for researchers to:
Co-author publications in high-impact journals
Present at leading conferences including AGU, EGU, and AMS
Contribute directly to federal air quality and climate policy initiatives
Mentor students and engage in interdisciplinary team science
