AI Models for Earthsystem AI Models for Earthsystem Argonne National Laboratory In-person · Lem[...]

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) and 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 models 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, as well as experience with diffusion‑based or other generative AI methods and multi‑modal embeddings, and a background in atmospheric science, especially weather modeling. Responsibilities Contribute 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. Develop and evaluate machine learning/computational approaches, synthesis activities, computational tools, compile results, and contribute to reports, publications, and documentation. 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 domains. 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 with 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 equivalent in geophysical sciences, computer science, machine learning, or a related field. Equal Employment Opportunity Statement As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law. #J-18808-Ljbffr Seeds Renewables