Argonne National Laboratory, a U.S. Department of Energy national laboratory located near Chicago, Illinois, has an opening for a highly motivated post-doctoral appointee at the Decision and Infratsructure Science (DIS) Division. Recently Argonne had successfully developed AI foundation models for weather and Seasonal-to-subseasonal forecasting, hydrology and urban digital twins. As we move many of these models from development to application to infrastructure and resilience workflows within DIS and supporting DOE and private sector utilities, we have started building out a team to support these fforts. A successful candidate will collaborate with scientists in DIS, Environmental Sciences division and Argonne leadership Computing Facility to further develop the AI foundation models, benchmarking and deploy them for applications.
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, hydrology). 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 and multimodal data training approaches, fine tuning and optimizing inference is desired. Background 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.
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
- Knowledge in training domain specific AI foundation models built using transformers, GNN and diffusion
- Technical knowledge in using HPC systems for visualization and analysis
- Technical knowledge of large, dynamical systems (preferability 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 and hydrological observational datasets, data assimilation techniques, and statistics.
- Ability to work and communicate with stakeholders from public and private sectors
- Some familiarity in subseasonal-to-seasonal modeling and or coupled atmosphere-ocean modeling
- 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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