AI Models for Earthsystem

Argonne National Laboratory•Lemont, IL

2h•Onsite

About The Position

Argonne National Laboratory has an opening for a highly motivated term position in the Decision & Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL) and AI foundation models, has demonstrated success in predicting weather. This position will collaborate with a group to further develop AERIS, coupling the model with ocean and land components, data assimilation, multi-modality and regional refinement. The role will utilize generative AI transformer models to create a calibrated ensemble system for S2S at high resolution (30-km and finer) to deliver probabilistic weather forecasts beyond 14 days, enabling 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). Familiarity with data and model parallelisms for distributed training on large GPU-based machines is essential. Experience with diffusion-based or other generative AI methods and multi-modal embeddings, as well as a background in atmospheric science, especially weather modeling, is also desired.

Requirements

PhD Degree or their equivalents in geophysical sciences, computer science, machine learning, or a related field.
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.
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.
Experience in scientific programming and data analysis.

Nice To Haves

Knowledge of large, dynamical systems (preferably the atmosphere).
Knowledge of atmospheric dynamics, process scale models, and numerical computation techniques is preferred.
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.

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, and contributes to reports, publications, and documentation.
Assists 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.
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.