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Original Job Post

Argonne National Laboratory · 2 days ago

Postdoctoral Appointee - Physics-Informed Machine Learning/Hydrology

Lemont, IL

Full-time

Onsite

Entry Level

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Responsibilities

Advance state-of-the-art physics-informed artificial intelligence and machine learning models for improving hydrologic systems modeling and near real-time forecasting.

Develop a framework and standardized benchmark suite for scalable and robust physics-informed AI/ML for next-generation hydrologic and hydrodynamic modeling.

Utilize leading high-performance computing platforms for model development.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

AI/MLNeural networksPhysics modelingHydrologyData analysisStatisticsVisualizationPyTorchTensorFlowHPC systemsScientific codingPythonJuliaFortranC++Core values modelingProblem-solvingEffective communicationOrganizational skillsTeamwork

Required

Recent PhD (typically completed within the last 3 years) in hydrology, civil engineering or a related field.

Knowledge of key approaches for embedding physics in AI/ML models, especially neural operators, physics-informed neural networks, hybrid modeling, and regularization techniques.

Experience with various neural network architectures (e.g., graph neural networks, autoencoders, generative adversarial networks, etc.)

Understanding of hydrologic and hydrodynamic processes and modeling.

Experience in applying AI/ML for hydrologic and hydrodynamic predictions

Experience in using AI/ML frameworks (e.g., PyTorch, TensorFlow, Flux, or similar) on HPC systems

Skilled in data analysis, statistics, and visualization, especially on large datasets.

Knowledge of developing flood observation training datasets from multiple sources.

Experience in writing and modifying scientific code in Python, Julia, Fortran, and C++.

Effective written and oral communication skills.

Effective organizational skills and the ability to coordinate across a broad spectrum of activities.

Demonstrated ability to work independently and in a team environment.

Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Company

Argonne National Laboratory

Glassdoor

4.4

Argonne National Laboratory conducts researches in basic science, energy resources, and environmental management.

Founded in 1946

Lemont, Illinois, USA

1,001-5,000 employees

http://www.anl.gov/

H1B Sponsorship

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