Job Description

Department: College of Science

Classification: Research Staff 12-month

Job Category: Research Staff

Job Type: Full-Time

Work Schedule: Full-time (1.0 FTE, 40 hrs/wk)

Location: Fairfax, VA

Workplace Type: Hybrid Eligible

Sponsorship Eligibility:Not eligible for visa sponsorship

Salary: Salary commensurate with education and experience

Restricted: Yes

Criminal Background Check: Yes

About the Department:

The Center for Satellite and Earth Science Research (CSER) advances Earth system science through cutting-edge satellite technology, data-driven research, and interdisciplinary collaboration. We deliver policy-relevant insights that enhance environmental resilience, support sustainable development, and empower the next generation of scientific leaders through meaningful research and mentorship. As a national leader in remote sensing, we provide reliable early warning systems that inform the public, save lives, and strengthen global resilience.

Statement on Building an Inclusive Culture
George Mason University College of Science (Mason Science) is committed to advancing all-inclusive excellence and fostering an environment free from discrimination, harassment, and retaliation throughout our STEM community. At Mason Science, our values include cultivating an organizational culture that promotes belonging, respect, and civility. We believe that varied opinions, cultures, and perspectives are what provides vibrancy, innovation and growth to an academic community. By prioritizing cultural responsiveness in academics, teaching, research, and global engagement, we strive to attract faculty and staff who exemplify the Mason Science mission and vision.

About the Position:

The primary purpose of this Research Scientist position is to take a role of code manager for NOAA/OAR/ARL’s model and tool repository. The Research Scientist will also conduct the developmental and testing work for the atmospheric composition models.

Responsibilities:

Leads Research in Air Quality Forecasting and Modeling

• Designs, implements, and manages research projects focused on regional and urban-scale air quality forecasting; and
• Collaborates with multi-disciplinary teams to integrate modeling outputs into operational decision-making systems.

Develop Advanced ML/AI Models for Air Quality Applications

• Applies machine learning (ML) and artificial intelligence (AI) techniques to enhance traditional chemical transport modeling systems;
• Improves prediction accuracy and computational efficiency through fine-resolution data fusion, bias correction, and model surrogate development; and
• Conducts experiments comparing AI-enhanced models with conventional methods and validate performance under various atmospheric scenarios.

Disseminate Research through Publications and Scientific Engagement

• Prepares manuscripts for submission to high-impact, peer-reviewed journals in atmospheric science, environmental engineering, or AI applications;
• Presents research findings at national and international scientific conferences, workshops, and stakeholder meetings; and
• Contributes to proposal development for external research funding, including NSF, NOAA, NASA, and EPA.

Required Qualifications:

• Bachelor’s degree in related field;
• Considerable experience (generally 5 years or longer) in atmospheric sciences; and
• Knowledge of Fortran, Python, R, Database (Postgres, MySQL, SQLite), Shell program language skills.

Preferred Qualifications:

• Knowledge of Community Multiscale Air Quality program (CMAQ), Sparse Matrix Operator Kerner Emissions program (SMOKE), Data Assimilation, Surrogate Tools, and other modeling skillsets.

Instructions to Applicants:

For full consideration, applicants must apply for Research Scientist at https://jobs.gmu.edu/. Complete and submit the online application to include three professional references with contact information, and provide a Cover Letter/Letter of Intent with Resume for review.

Posting Open Date: February 16, 2026

For Full Consideration, Apply by: March 2, 2026

Open Until Filled: Yes