Hybrid Satellite Cloud Research Scientist (Postdoc)

TopSciNet; Fort Collins (CO); Hybrid; USD 67,000 - 70,000; Full time

Job summary

Colorado State University’s Cooperative Institute for Research in the Atmosphere (CIRA) seeks a Postdoctoral Fellow to develop physics-based and machine learning methods for environmental satellite remote sensing applications. This full-time position supports NOAA-funded research projects, including global 3D satellite cloud studies and multi-scale weather applications.

Responsibilities include research on cloud properties estimation, method development, validation with in-situ data, and

Qualifications

• Ph.D. in Meteorology, Atmospheric Sciences, or a closely related STEM field (defense must be completed before start).
• Experience in satellite remote sensing related to atmospheric clouds.
• Proficiency in reading, writing, and manipulating scientific datasets for meteorological applications.
• Experience working in team environments and effectively communicating scientific results.
• Preferred: experience with in-situ weather and climate measurements; knowledge of radiative transfer models; writing for scientific publications.

Responsibilities

• Conduct research on satellite-based estimation of global cloud properties using geostationary/polar-orbiting data.
• Develop and apply physics/statistics-based and AI/ML methods for environmental remote sensing.
• Validate research results with in-situ measurements and intercomparisons with multiple satellite datasets.
• Contribute to scientific publications, conference presentations, and collaborative research projects.
• Collaborate with operational partners, product end users, and interdisciplinary teams.

Skills

Python

ML methods

Datasets handling

Team collaboration

Scientific communication

Satellite remote sensing

Education

PhD in Meteorology or Atmospheric Sciences

Tools

Python

Job description

Colorado State University’s Cooperative Institute for Research in the Atmosphere (CIRA) seeks a Postdoctoral Fellow to develop physics-based and machine learning methods for environmental satellite remote sensing applications. This full-time position supports NOAA-funded research projects, including global 3D satellite cloud studies and multi-scale weather applications.

Responsibilities include research on cloud properties estimation, method development, validation with in-situ data, and