Postdoctoral Appointee - AI Foundation Models for Atmospheric Science

Argonne National Laboratory • Lemont, IL

1d • Onsite

About The Position

Argonne National Laboratory has an opening for a highly motivated postdoctoral appointee in the Decision and Infrastructure Sciences Division. The role focuses on machine learning (ML), specifically deep learning (DL), which has shown success in weather prediction. The successful candidate will collaborate with a group that implemented AERIS, a state-of-the-art seasonal-to-subseasonal (S2S) weather model AI model. Responsibilities include evaluating AERIS at S2S scales, coupling an ocean component to the model, data assimilation, and regional refinement. A key aspect of this position is utilizing generative AI to create a calibrated ensemble system for S2S at high resolution (30-km) to deliver probabilistic weather forecasts beyond 14 days, aiming for actionable, local-scale impacts on infrastructure and communities. This is a one-year position that can be extended to two years.

Requirements

• Recent or soon-to-be-completed PhD (completed within the last 0-5 years) in geophysical sciences, computer science, or machine learning with 0 to 2 years of experience
• Knowledge of deep learning, PyTorch/JAX, and scaling deep learning models to large GPU-based machines
• Technical knowledge in using HPC systems for visualization and analysis
• Technical knowledge of large, dynamical systems (preferably the atmosphere)
• Knowledge and experience in writing scientific code
• Skills in clear, concise writing of technical papers, and interacting and communicating effectively with colleagues
• Problem solving skills
• Organizational skills and flexibility in coordinating a broad spectrum of activities
• Knowledge of atmospheric dynamics, process scale models, and numerical computation techniques
• Knowledge of data analysis
• Knowledge of using atmospheric observational datasets, data assimilation techniques, and statistics
• Familiarity subseasonal-to-seasonal modeling and or coupled atmosphere-ocean modeling
• Ability to work and communicate with stakeholders from public and private sectors
• Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork

Nice To Haves

• Experience using diffusion-based or other generative AI methods
• Experience in atmospheric science, especially weather modeling

Responsibilities

• Contributes technical expertise through analysis and support for programs and projects associated with machine learning, HPC, and computational problems related to earth system science and other dynamical systems.
• Development, evaluation, and applying machine learning/computational approaches, synthesis activities, computational tools, compiling results, preparing reports, publications, and documentation.
• Applying and developing machine learning-based weather models for the S2S timeframe with an emphasis on generative AI techniques, evaluating such models, and working with a team of scientists interested in pushing the boundary of predictability.

Benefits

• Comprehensive benefits are part of the total rewards package.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 1,001-5,000 employees