Postdoctoral Appointee - Convective Storms and Severe Weather

Argonne National Laboratory•Lemont, IL

1d•Onsite

About The Position

The Environmental Science Division at Argonne National Laboratory seeks a highly motivated postdoctoral researcher to advance the understanding and prediction of severe weather using both physics-based and AI/ML approaches. This position focuses on high-resolution Earth system modeling and the analysis of convective storms and weather and hydrological hazards to improve predictability, particularly at subseasonal and seasonal lead times. The successful candidate will lead simulations of convective systems using high-resolution regional and global models, evaluate model performance, and develop innovative approaches to enhance predictive skill. The role also involves integrating AI/ML techniques to advance physical parameterizations and improve severe weather prediction skills.

Requirements

Ph.D. (typically completed within the past 0–5 years) in Atmospheric Science or a related field
Demonstrated expertise in one or more of the following areas: convective storms, precipitation processes, severe winds, land–atmosphere interactions, or aerosol–cloud interactions
Strong experience in numerical modeling and high-performance computing
Experience applying AI/ML methods to model development, with strong programming skills (e.g., Python, Fortran, C++)
Knowledge of data analysis techniques and statistical methods
Proven scientific writing and oral communication skills
Ability to work both independently and collaboratively in a team environment, with creativity and strong organizational skills
Commitment to upholding Argonne’s core values: Impact, Safety, Respect, Integrity, and Teamwork

Nice To Haves

Experience applying AI/ML algorithms to severe weather prediction
Proficiency in C++ and experience running simulations on GPU-based systems
Familiarity with convection-permitting models such as E3SM and ERF

Responsibilities

Conducting high-resolution model simulations of convective storms
Contributing to model development using AI/ML approaches in areas such as clouds, turbulence, land–atmosphere interactions, and aerosol–cloud interactions
Analyzing model outputs alongside observational datasets to advance physical understanding
Publishing research findings in peer-reviewed journals and presenting results at scientific meetings
Collaborating closely with scientists in the broad Earth system Science community and with industry partners