Postdoctoral Appointee - AI Foundation Models For Atmospheric Science

Argonne National Laboratory, a U.S. Department of Energy national laboratory located near Chicago, Illinois, has an opening for a highly motivated postdoctoral appointee in the Decision and Infrastructure Sciences Division.

Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction at a fraction of the computational cost. Recently Argonne successfully implemented, AERIS, a state-of-the-art seasonal-to-subseasonal (S2S) weather model AI model. A successful candidate will collaborate with this group to evaluate AERIS at S2S scales, couple ocean component to the model, data assimilation and regional refinement. In particular, this position will utilize generative AI to create a calibrated ensemble system for S2S at high resolution (30-km) to deliver probabilistic weather forecasts beyond 14 days to allow for actionable, local-scale impacts on infrastructure and communities.

The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as well as experience in atmospheric science, especially weather modeling, are particularly sought after. This is a one-year position that can be extended to two years that we want to fill immediately.

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. In particular, this position is for projects related to 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.

Position 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

• A successful candidate must have the ability to model Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

The expected hiring range for this position is $72,879.00-$121,465.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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