Postdoctoral Fellow I

Visa sponsorship and relocation assistance available.

Application closes at 12:00am MST on 2026-08-30. Hiring range: 82,650.00 USD - 82,650.00 USD. Final salary based on education, experience, and skills relevant to the role.

About The Role

We seek a motivated postdoctoral fellow to contribute to cutting-edge research in coupled atmosphere-aerosol-land data assimilation and forecasting within NSF NCAR’s Mesoscale and Microscale Meteorology (MMM) Laboratory. The fellow will help advance satellite data assimilation capabilities and conduct cycling analysis and forecast experiments using a variety of real-world observations. The primary research platform will be our next-generation, online-coupled community analysis and forecasting system, based on the Model for Prediction Across Scales and interfaced with the Joint Effort for Data Assimilation Integration.

The fellow will also be encouraged to explore various algorithms, including artificial intelligence and machine-learning approaches, through targeted case studies. Depending on project needs and the fellow’s expertise and interests, the work may involve collecting and processing observational and model datasets; advancing data assimilation algorithms and observation operators; and developing postprocessing tools, cycling infrastructure, and workflow scripts.

The fellow will work both independently and collaboratively in an interdisciplinary research environment, apply sound scientific interpretation to experimental results, advance the use of satellite observations in atmospheric composition and/or weather forecasting, and publish findings in peer-reviewed journals.

Responsibilities

• Conduct independent and collaborative research in satellite data assimilation and coupled analysis and forecasting.
• Design and perform cycling analysis and forecast experiments using the MPAS-GOCART2G-JEDI system to evaluate the impact of remote sensing observations. This work may include developing or improving capabilities for data collection, processing, postprocessing, visualization, and end-to-end cycling workflows.
• Explore innovative data processing or data assimilation methods, including AI/ML approaches, through targeted case studies.
• Analyze and scientifically interpret experimental results; evaluate the impacts of assimilated observations and methodological developments on atmospheric analyses and forecasts; diagnose system performance; identify limitations; and recommend improvements based on quantitative verification and scientific understanding.
• Communicate research findings and support project activities. Prepare research results for presentation at scientific conferences and publication in peer-reviewed journals.
• Assist with the preparation of scientific proposals, project progress reports, and related documentation, as needed.

Requirements

• Ph.D. degree within the last 5 years or expected within the next 6 months in atmospheric science, computer science, statistics, or a related area.
• Knowledge of data assimilation algorithms and cycling experiments.
• Skill in scripting languages (e.g., Python/shell), particularly for testing and running workflows on supercomputers.
• Knowledge of satellite meteorology or cloud microphysics.
• Demonstrated ability to work independently and collaboratively as part of a research team.
• Excellent time management and organization skills.
• Excellent written and oral communication skills.
• Knowledge of numerical weather prediction modeling.

Qualifications

• Desired: Demonstrated knowledge of remote sensing data assimilation.
• Desired: Demonstrated knowledge of satellite retrieval processes.

Benefits

• Medical, dental, vision, and short & long-term disability insurance.
• Generous retirement plan (UCAR contributes 10% of base salary).
• Minimum 20 days of annual PTO, 10 paid holidays, and 12 weeks of paid parental leave.
• FAMLI Leave and Tuition Assistance Program.
• Public Service Loan Forgiveness (PSLF) eligible employer.

Pay

Hiring range: 82,650.00 USD. Final salary based on education, experience, and skills relevant to the role.

Schedule

Term position: 6 months or more (fixed term). All hybrid staff are expected to work on-site approximately 3 days per week.