What are the responsibilities and job description for the Postdoctoral Appointee: Physics-Informed Machine Learning Applications for Hydrology position at Argonne National Laboratory?
Argonne National Laboratory, a U.S. Department of Energy National Laboratory located near Chicago, Illinois, has an opening for a Postdoctoral Appointee specialized in deep learning applications for hydrology and hydrodynamic modeling, especially 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 Argonnes Center for Climate Resilience and Decision Science (CCRDS). Model development will utilize the leading high performance computing platforms at ALCF ( https : / / www.alcf.anl.gov ) and other Department of Energy computing platforms.
This is a two-year position that we want to fill immediately.
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.
- Position Requirements
- Completed PhD within the last 0-5 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 writing and modifying scientific code in Python, Julia, Fortran, and C Effective written and oral communication skills
- Strong 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 Argonnes Core Values : Impact, Safety, Respect, Integrity, and Teamwork
- Job Family
Postdoctoral Family
Postdoctoral Appointee
Long-Term (Fixed Term)
Full time
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