Research Scientist in Physics-Informed AI/ML

2 weeks ago

Lemont, Illinois, United States Argonne National Laboratory Full time

Argonne National Laboratory, a premier U.S. Department of Energy National Laboratory, is seeking a Research Scientist with expertise in physics-informed artificial intelligence and machine learning within the Department of Hydrology, Environmental Science Division.

The successful candidate will focus on enhancing cutting-edge physics-informed AI/ML models aimed at optimizing hydrologic systems modeling and facilitating near real-time forecasting with high precision. This initiative seeks to establish a comprehensive framework and a standardized benchmark suite for scalable and resilient physics-informed AI/ML applications in next-generation hydrologic and hydrodynamic modeling. The appointee will collaborate with a team of scientists engaged in this endeavor, supported by Argonne's Center for Climate Resilience and Decision Science (CCRDS). Model development will leverage the leading high-performance computing resources available at Argonne and other Department of Energy facilities.

Position Requirements

  • Recent PhD (typically completed within the last 3 years) in hydrology, civil engineering, or a related discipline.
  • Proficiency in key methodologies for integrating physics into AI/ML models, particularly neural operators, physics-informed neural networks, hybrid modeling, and regularization techniques.
  • Experience with diverse neural network architectures (e.g., graph neural networks, autoencoders, generative adversarial networks, etc.).
  • Solid understanding of hydrologic and hydrodynamic processes and modeling techniques.
  • Demonstrated experience in applying AI/ML for hydrologic and hydrodynamic forecasting.
  • Familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow, Flux, or similar) on high-performance computing systems.
  • Strong skills in data analysis, statistics, and visualization, particularly with large datasets.
  • Knowledge of developing flood observation training datasets from various sources.
  • Experience in writing and modifying scientific code in Python, Julia, Fortran, and C++.
  • Excellent written and verbal communication abilities.
  • Strong organizational skills and the capacity to coordinate across a wide range of activities.
  • Proven ability to work independently as well as collaboratively within a team environment.
  • Commitment to embodying Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
This position description outlines the general nature and level of work but is not intended to be an exhaustive list of all activities, duties, and responsibilities required of the job incumbent. Consequently, the job incumbent may be required to perform additional duties as assigned.

Job Family
Postdoctoral Family

Job Profile
Research Scientist

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

As an equal employment opportunity and affirmative action employer, and in alignment with our core values of impact, safety, respect, integrity, and teamwork, Argonne National Laboratory is dedicated to fostering a diverse and inclusive workplace that promotes collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages applications from minorities, women, veterans, and individuals with disabilities. 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.

All offers of employment at Argonne 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 may require the individual to obtain 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.

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