Postdoctoral Researcher - AI for Earth System Sciences

Argonne National Laboratory • Lemont, IL

8d • $72,879 - $121,465 • Onsite

About The Position

Argonne National Laboratory has an opening for a highly motivated post-doctoral appointee in the Decision and Infrastructure Science (DIS) Division. The role involves collaborating with scientists to further develop, benchmark, and deploy AI foundation models for applications in infrastructure and resilience workflows. The position is particularly focused on applying and developing machine learning-based weather models for the subseasonal-to-seasonal (S2S) timeframe, with an emphasis on generative AI techniques and pushing the boundaries of predictability. This is a one-year position that can be extended to two years and is to be filled immediately.

Requirements

• Recent or soon-to-be-completed PhD (completed within the last 0-5 years) in geophysical sciences, computer science, or machine learning.
• 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.
• Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Nice To Haves

• Expertise in data and model parallelisms for distributed training on large GPU-based machines.
• Experience using diffusion-based or other generative AI methods and multimodal data training approaches, fine tuning and optimizing inference.
• Background in atmospheric science, especially weather modeling.
• Some familiarity in subseasonal-to-seasonal modeling and or coupled atmosphere-ocean 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.
• 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