Argonne National Laboratory, a U.S. Department of Energy National Laboratory located near Chicago, Illinois, has an opening for a highly motivated postdoctoral appointee in the Environmental Science Division.

Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction at a fraction of the computational cost.

A group of scientists at Argonne in collaboration with UCLA have successfully implemented a state-of-the-art ML weather model called Stormer. The candidate selected for this role will collaborate with this group of scientists to extend the predictability of Stormer to the subseasonal-to-seasonal (S2S) timeframe. This position will utilize generative AI to create a calibrated ensemble system for S2S at high resolution (30-km) to deliver probabilistic weather forecasts beyond 14 days to allow for actionable, local-scale impacts on infrastructure and communities.

In this role, you can expect to:

Contribute 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.Develop, evaluate, and apply machine learning/computational approaches, synthesis activities, computational tools, compiling results, preparing reports, publications, and documentation.In particular, focus efforts on 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.

For more information, please see:

Stormer model ICLR best paper award: (https://www.climatechange.ai/papers/iclr2024/7)Argonne press release: (https://www.anl.gov/article/argonne-develops-new-kind-of-ai-model-for-weather-prediction)

Position Requirements

Required skills and qualifications:

Completed PhD (typically completed within the last 0-5 years) in geophysical sciences, atmospheric science, computer science, or related fieldExperienced in deep learning, PyTorch or JAX, and scaling deep learning models to large GPU-based machinesTechnical knowledge of large, dynamical systems (preferability the atmosphere and/or ocean)Expertise in data and model parallelisms for distributed training on large GPU-based machinesExpertise in clear, concise writing of technical papers, and interacting and communicating verbally and orally effectively with colleaguesAbility to model Argonne's core values of impact, safety, respect, integrity and teamwork

Preferred skills and qualifications:

Candidates with experience using diffusion-based or other generative AI methods as well as experience in atmospheric science, especially weather modeling, are particularly sought afterTechnical knowledge in using HPC systems for visualization and analysisExperience in writing scientific codeEffective problem-solving skills, organizational skills, and flexibility in coordinating a broad spectrum of activitiesKnowledge of atmospheric dynamics, process scale models, and numerical computation techniquesKnowledge of data analysisKnowledge of using atmospheric observational datasets, data assimilation techniques, and statisticsFamiliarity subseasonal-to-seasonal modeling and or coupled atmosphere-ocean modelingAbility to work and communicate with stakeholders from public and private sectors

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

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