CIRES / NOAA Global Systems Laboratory, Post Doctoral Associate in Numerical Weather Prediction (Microphysics)

CIRES is an internationally recognized leader in innovative environmental science and research and is located at the University of Colorado Boulder. At CIRES, more than 900 environmental science professionals work to understand the dynamic Earth system, including people's relationship with the planet. CIRES has partnered with NOAA since 1967, and our areas of expertise include weather and climate, changes at Earth's poles, air quality and atmospheric chemistry, water resources, solid Earth sciences, and more. Our vision is to be instrumental in ensuring a sustainable future environment by advancing scientific and societal understanding of the Earth system.

The Global Systems Laboratory (GSL) is one of ten NOAA Research laboratories and is located in Boulder, Colorado. GSL researchers develop innovative weather and air quality models, state-of-the-science decision support tools and visualization systems and use high-performance computing technology to support a Weather-Ready Nation. GSL strives to advance its workforce scientifically, technically, and professionally. We are also committed to fostering a collaborative workplace and a strong foundation for career growth.

• Conduct hypothesis-driven research to improve the physical fidelity, accuracy, and/or efficiency of microphysics parameterizations within numerical weather prediction models, particularly as it relates to precipitating phenomena (60%).
• Collaborate with the lead microphysics parameterization developer and other physics parameterization developers within the branch to inform future development activities (20%).
• Prepare and/or contribute to the preparation of briefing materials and presentations for branch management, internal meetings, conferences, and workshops, and regularly publish scientific content (15%).
• Perform other relevant duties as necessary (5%).

• This is a two-year appointment.
• Visa sponsorship is not offered for this position. Given project deliverables, candidates must be eligible for full-time work and pass a federal clearance background check by the targeted start date of December 1, 2026.
• This position will be rostered in CIRES at the University of Colorado Boulder but will be physically situated in the NOAA laboratory facility at the David Skaggs Research Center, 325 Broadway, Boulder, CO 80305.
• If you are the finalist, you will be required to pass a federal background check. Non-US citizens without green cards will have limited building access and require a federal escort in the building.
• Travel to workshops and conferences (domestically and possibly internationally) is expected at least once per year.

• CIRES offers a generous compensation package
• The annual hiring salary range for this position is $65,000- $70,000. Salary is commensurate with education and experience.
• This position can accommodate a hybrid work modality.
• Some relocation funds are available to help offset expenses, following CIRES and the University of Colorado's relocation processes and procedures.
• University of Colorado Boulder offers a robust training curriculum, opportunities for professional development and a Mentorship Program.
• Boulder is a vibrant community with access to mountain parks, dog parks, miles of trails, rivers, lakes, cafes, restaurants, boutiques, theaters, museums, and sports venues.
• As an employee at CU Boulder, you will have free access to the regional public transit system, an outstanding network of buses and light rail systems that service Boulder and connect to Denver, the Denver airport, and surrounding communities.

• A Ph.D. in atmospheric science or a relevant field of study (the defense and thesis revisions must be successfully completed by the targeted start date of December 1, 2026).

• Proficiency with numerical weather and/or climate prediction.
• Proficiency in Fortran and/or C++, Python, high-performance computing, and Linux computing environments.
• Strong skills with data processing (e.g., of standard weather-model data formats such as NetCDF and GRIB2) and visualization of model output.
• A willingness to collaborate with the developers of relevant physical parameterizations that interact with microphysics in the branch, across NOAA, and in the broader community to improve the physical consistency within a physics parameterization suite.
• The ability to work as part of a team of diverse scientists, researchers, developers, engineers, and staff in a research environment.
• The ability to optimally communicate and collaborate within the branch, division, lab, and broader community.
• The ability to consistently promote, support, work, and act in a manner in support of CIRES' vision, mission and values.

Please note that while the position details both required and preferred skills and experience, we invite applicants to apply even if they do not have the preferred skills and experience outlined in this section. If you meet the requirements and have passion for the work, you are encouraged to apply. We encourage on the job training for any additional skills or knowledge that become relevant to the position.

• Experience using and/or developing microphysical parameterizations (including bin, bulk, Lagrangian) in a numerical weather or climate model with a specific focus on understanding, improving, or developing microphysical parameterizations using relevant observations.
• Enthusiasm for conducting innovative numerical weather prediction model development to improve their physical realism and predictive skill for relevant forecast problems, including heavy precipitation, thunderstorms and their hazards, fog, freezing rain, aircraft icing, tropical cyclones, atmospheric rivers, lake-effect snow, and/or orographically induced precipitation.
• Knowledge of basic statistical learning techniques (such as neural networks).
• Experience participating in and/or working with observations from relevant field campaigns.
• Experience with community modeling approaches for numerical weather prediction, including developing physics parameterizations in an interoperable way such that they can be used with multiple host models.