Postdoctoral Appointee - Land-atmospheric interactions and hydrological extremes
About The Position
The Argonne National Laboratory at U.S. Department of Energy supported research programs invites applications for a Postdoctoral Research Associate position focused on understanding the relative role of land–atmosphere interactions in S2S predictability; impacts on boundary layer processes, aerosol-cloud interactions, precipitation, and hydrological extremes, including feedback mechanisms that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling. The successful candidate will work with observational and modeling teams using data from the Atmospheric Radiation Measurement (ARM) User Facility including the Bankhead National Forest (BNF) and Desert-Urban System Integrated Atmospheric Monsoon (DUSTIEAIM) observational datasets, together with advanced modeling frameworks such as Energy Exascale Earth System Model (E3SM) or data-driven AI models.
Requirements
- Completed or soon-to-be-completed Ph.D. within the last 0-5 years in Atmospheric Science, Meteorology, Climate Science, Applied Mathematics, Data Science, or a related field with strong quantitative and computational research experience
- Strong background in atmospheric dynamics, turbulence, land–atmosphere interactions, cloud physics, or precipitation processes
- Experience working with large observational or model datasets
- Experience with developing AI/ML Deep Learning models, working with agentic workflows, model training and other emerging AI techniques and tools
- Programming experience in Python, C++, or similar scientific computing environments
Responsibilities
- Understanding the relative role of land–atmosphere interactions in S2S predictability
- Analyzing impacts on boundary layer processes, aerosol-cloud interactions, precipitation, and hydrological extremes
- Investigating feedback mechanisms that influence the development, intensification, and persistence of extreme events
- Utilizing observational datasets, machine learning, and Earth system modeling
- Working with observational and modeling teams using data from the Atmospheric Radiation Measurement (ARM) User Facility including the Bankhead National Forest (BNF) and Desert-Urban System Integrated Atmospheric Monsoon (DUSTIEAIM) observational datasets
- Utilizing advanced modeling frameworks such as Energy Exascale Earth System Model (E3SM) or data-driven AI models
- Demonstrated ability to publish scientific research
- Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
Benefits
- Comprehensive benefits are part of the total rewards package.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
