Postdoctoral Fellow: Satellite Data Assimilation

University Corporation for Atmospheric Research (UCAR); Boulder (CO); Hybrid; USD 74,000 - 91,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Paid time off

Tuition assistance

Job summary

UCAR seeks a motivated postdoctoral fellow to contribute to research in coupled atmosphere-aerosol-land data assimilation and forecasting within NSF NCAR MMM Laboratory. The fellow will advance satellite data assimilation capabilities and conduct cycling analysis and forecast experiments using the MMM system.

The role involves exploring AI/ML methods, developing data processing and cycling workflows, and collaborating on interdisciplinary projects.

Qualifications

• Ph.D. degree within the last 5 years or expected within 6 months in atmospheric science, computer science, statistics, or related area.
• Knowledge of data assimilation algorithms and cycling experiments; ability to design experiments and interpret results.
• Experience scripting for HPC workflows (Python/Shell).
• Excellent communication and collaboration in a research setting.

Responsibilities

• Conduct independent and collaborative research in satellite data assimilation and coupled analysis and forecasting.
• Design and run cycling analysis and forecast experiments using the MMM system to evaluate remote sensing observations.
• Develop data collection, processing, postprocessing, visualization, and end-to-end cycling workflows.

Skills

Data assimilation

Python / Shell

Satellite meteorology

Independent work

Time management

Communication skills

NWP modeling

Education

Ph.D. in atmospheric science / related field

Tools

MPAS-GOCART2G-JEDI

Job description

UCAR seeks a motivated postdoctoral fellow to contribute to research in coupled atmosphere-aerosol-land data assimilation and forecasting within NSF NCAR MMM Laboratory. The fellow will advance satellite data assimilation capabilities and conduct cycling analysis and forecast experiments using the MMM system.

The role involves exploring AI/ML methods, developing data processing and cycling workflows, and collaborating on interdisciplinary projects.