MMM Postdoctoral Fellow I

Position Type:
Term - 6 months or more (Fixed Term) Visa Sponsored Job:
Yes Relocation Assistance Eligible:
Yes Hiring Range: 82,650.00 USD - 82,650.00 USD
* Final salary and rates are based on education, experience, and skills relevant to the role. Final date to receive applications at 12:00am MST on:

UCAR is excited to announce the job opening for the Mesoscale and Microscale Meteorology (MMM) Postdoctoral Fellow role. This position will contribute to research activities within NCAR’s MMM laboratory related to developing a GPU-compatible and auto-differentiable version of the Model for Prediction Across Scales (MPAS) using a higher-level, interpreted language such as JAX. The MMM Postdoctoral Fellow will interact with NCAR scientists, staff, and other postdoctoral fellows to advance the research over the position’s one-year term, and will be encouraged to present at scientific conferences, publish scientific papers, and pursue professional development activities.

The mission of MMM is to lead and enable research that advances the understanding of weather and to apply this knowledge to benefit society. In support of this mission, MMM strives to produce accurate and effective computational models, data assimilation systems, and representations of unresolved weather model processes from local to global scales. We also conduct theoretically driven social science and interdisciplinary research that connects to the hazardous weather predictability and prediction capabilities.

With extensive external contributions, MMM’s efforts have included development of the Weather Research and Forecasting (WRF) model, the Model for Prediction Across Scales (MPAS), and sophisticated codes for cloud-resolving and eddy-resolving simulations. MMM continues to emphasize boundary‑layer, turbulence and cloud–microphysics research. Eddy-resolving simulations of mesoscale phenomena such as tropical cyclones, mesoscale convective systems and fronts in the atmosphere and ocean enable research of multi‑scale dynamics.,

i.e., a deeper understanding of interactions across a continuous spectrum of mesoscale and microscale atmospheric motions.

Final date to receive applications:
This position will be posted until 11:59pm MT on Tuesday, August 11, 2026. Applications will not be accepted past this date. Required application materials:
Resume - preferably in PDF Format Questionnaire - to be completed when submitting your application In lieu of a traditional cover letter, answer the following prompts that address the critical skills needed for this position. Your answers will be read and weighed equally to your Resume/CV and should provide specific, detailed, and informative responses based on your direct and previous work experiences.

Please keep responses to 1-3 paragraphs per prompt. Summarize your background in the computational simulation of fluid systems, especially the atmosphere. Did you write code? Did you perform numerical analysis of the computational solver? Did you address optimization and scaling of the computations? Did you work with or develop codes that were differentiable? How big were the problems you worked on?

Please briefly describe your experience related to the use of GPUs for computational fluid simulations. (See above for possible dimensions your answer might consider.) Suppose you had a year to do unconstrained research related to computational simulations of the atmosphere. What would you do, and why is it interesting? Background checks are conducted for candidates selected for hire. Learn more.

This position is open to candidates seeking in-person or hybrid (combination of 3 days in-person and 2 days of remote) opportunities. UCAR requires ALL positions to be performed within the U.S., excluding U.S. Territories.

Key Responsibilities

• Conduct independent and collaborative research in machine learning for prediction of the Earth system or its components.
• In collaboration with the PANDA-C team in MMM, develop machine‑learning techniques that improve 0-12 h predictions of atmospheric cloud relative existing MPAS-JEDI baseline.
• Thes…