AI Models for Earthsystem

Responsibilities

• Contributes technical experience 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.
• Develops and evaluates machine learning/computational approaches, synthesis activities, computational tools, compiling results, contributes to reports, publications, and documentation .
• In particular, this position will assist on projects related to applying and developing machine learning-based weather models for the S2S time frame with an emphasis on generative AI techniques, evaluating such models, and working with a team of scientists.
• Position Requirements
• Experience with deep learning, PyTorch/ JAX, and scaling deep learning models to large GPU-based machines.
• Experience building, training and running inferences with large AI foundation models for science domain.
• Technical knowledge in using HPC systems for visualization and analysis.
• Knowledge of large, dynamical systems (preferably the atmosphere), is desirable.
• Skills in clear, concise writing of technical papers, and interacting and communicating effectively with colleagues.
• Some 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 is preferred.
• Experience in scientific programming and data analysis.
• Knowledge of using atmospheric observational datasets, data assimilation techniques, and statistics is preferred.
• Familiarity sub-seasonal-to-seasonal modeling and or coupled atmosphere-ocean modeling is desirable.
• Ability to work and communicate with stakeholders from public and private sectors.
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.
• Minimum Education/Experience Requirements: PhD Degree or their equivalents in geophysical sciences, computer science, machine learning, or a related field.
• Job Family
• Research Development (RD)
• Job Profile
• Atmospheric & Earth Science 2
• Worker Type
• Long-Term (Fixed Term)
• Time Type
• Full time
• The expected hiring range for this position is $94,486.00 - $147,398.94.
• Click here to view Argonne employee benefits!
• Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in

Benefits

Additional Information

Argonne National Laboratory, a U.S. Department of Energy national laboratory located near Chicago, Illinois, has an opening for a highly motivated term position in the Decision & Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL) an AI foundation models, has demonstrated success in predicting weather for 1-14 days with skill on par with numerical weather prediction. Recently Argonne successfully implemented, AI foundation models for medium range weather forecasting (STORMER) and AERIS, a state-of-the-art Seasonal-to-sub-seasonal weather model AI model. A successful candidate will collaborate with this group to further develop AERIS, coupling the model with ocean and land components, data assimilation, multi-modality and regional refinement. In particular, this position will utilize generative AI transformer model to create a calibrated ensemble system for S2S at high resolution (30-km and finer) to deliver probabilistic weather forecasts beyond 14 days to allow for actionable, local-scale impacts on infrastructure and communities. The ideal candidate has 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). Some familiarity in data and model parallelisms for distributed training on large GPU-based machines is essential. who have experience with diffusion-based or other generative AI methods and multi-modal embeddings, as well as a background in atmospheric science, especially weather modeling.

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