Postdoctoral Researcher - AI for Earth System Sciences

Argonne National Laboratory; Lemont (IL); On-site; USD 73,000 - 121,000; Full time

Benefits offered by this job

Comprehensive benefits

Equal opportunity employer

Job summary

Argonne National Laboratory in Illinois invites applications for a postdoctoral appointee in the Decision and Infrastructure Science Division to advance AI foundation models for weather and hydrology and deploy them within infrastructure and resilience workflows.

The successful candidate will collaborate across divisions, develop and benchmark diffusion-based and transformer models, and contribute to reports and publications.

Qualifications

• PhD completed or near completion in geophysical sciences, computer science, or machine learning.
• Experience developing and verifying deep learning models for large dynamical systems.
• Knowledge of data and model parallelism for distributed training on GPUs.
• Familiarity with diffusion-based or generative AI methods and multimodal data training.
• Background in atmospheric science or weather modeling is valued.
• Strong scientific writing and ability to communicate with colleagues.

Responsibilities

• Develop, evaluate, and apply ML/compute approaches for weather and earth system science.
• Benchmark AI foundation models and deploy them for S2S forecasting workflows.
• Collaborate across DIS, Environmental Sciences, and leadership computing facilities.
• Prepare reports, publications, and documentation of results.

Skills

Deep learning

PyTorch

JAX

Large-scale training

Transformers

GNN

Diffusion models

HPC systems

Atmospheric dynamics

Data analysis

Data assimilation

Scientific writing

Effective communication

Problem solving

Team collaboration

Education

PhD in geophysical sciences, CS, or ML

Tools

PyTorch

JAX

HPC environments

Job description

Argonne National Laboratory, a U.S. Department of Energy national laboratory located near Chicago, Illinois, has an opening for a highly motivated post-doctoral appointee at the Decision and Infratsructure Science (DIS) Division. Recently Argonne had successfully developed AI foundation models for weather and Seasonal-to-subseasonal forecasting, hydrology and urban digital twins. As we move many of these models from development to application to infrastructure and resilience workflows within DIS and supporting DOE and private sector utilities, we have started building out a team to support these fforts. A successful candidate will collaborate with scientists in DIS, Environmental Sciences division and Argonne leadership Computing Facility to further develop the AI foundation models, benchmarking and deploy them for applications.

The ideal candidate would be 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, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods and multimodal data training approaches, fine tuning and optimizing inference is desired. Background in atmospheric science, especially weather modeling, are particularly sought after.

This is a one-year position that can be extended to two years that we want to fill immediately. 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. In particular, this position is for 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.

Position Requirements

• Recent or soon-to-be-completed PhD (completed within the last 0-5 years) in geophysical sciences, computer science, or machine learning with 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.
• Some familiarity in subseasonal-to-seasonal modeling and or coupled atmosphere-ocean modeling.
• A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Job Family

• Postdoctoral Job Profile
• Postdoctoral Appointee Worker Type Long-Term (Fixed Term)
• Time Type Full time

Compensation and Benefits

• The expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only.
• The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs.
• Additionally, comprehensive benefits are part of the total rewards package.
• 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.
• 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 United States Department of Energy Order 486.1A.
• You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.
• All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis.
• Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements.
• Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.