- 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.
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Postdoctoral Appointee - Lemont, IL, United States - Argonne National Laboratory
Description
Postdoctoral Appointee -- Physics-Informed Machine Learning/Hydrology page is loaded
Postdoctoral Appointee -- Physics-Informed Machine Learning/Hydrology
Apply locations Lemont, IL USA time type Full time posted on Posted 3 Days Ago job requisition idArgonne National Laboratory, a U.S. Department of Energy National Laboratory, has an opening for a Postdoctoral Appointee specialized in physics-informed machine learning at the Department of Hydrology, Environmental Science Division.
The Postdoctoral Appointee will work toward advancing state-of-the art physics-informed artificial intelligence and machine learning (AI/ML) models to improve hydrologic systems modeling and near real-time forecasting at a high resolution. The project aims to develop a framework and standardized benchmark suite for scalable and robust physics-informed AI/ML for next-generation hydrologic and hydrodynamic modeling. The appointee will join a group of scientists working on this project supported by Argonne's Center for Climate Resilience and Decision Science (CCRDS). Model development will utilize the leading high performance computing platforms at ALCF ( ) and other Department of Energy computing platforms.
Position Requirements
This position description documents the general nature and level of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required of job incumbent. Consequently, job incumbent may be required to perform other duties as assigned.
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Long-Term (Fixed Term)Time Type
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