Postdoctoral Research Associate - AI for Hydrological Modeling

About the role

The Computational Hydrology and Atmospheric Science (CHAS) Group at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral Research Associate in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI).

Major Duties/Responsibilities

• Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions.
• Design and implement physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction.
• Conduct multimodal, multiscale data analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation.
• Collaborate with a multidisciplinary team of hydrologists, Earth scientists, and computational scientists to leverage leadership-class computing resources for large-scale model training, testing, and deployment.
• Contribute to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery.
• Publish research findings in high-impact journals and present results at national and international conferences.
• Engage with collaborators across DOE laboratories, universities, and partner agencies to broaden the applications of AI-enabled hydrological modeling.
• Ensure compliance with ORNL’s safety, security, quality, and environmental standards while carrying out all research activities.

Technical Questions

Please contact Dan Lu lud1@ornl.gov

Basic Qualifications

• A Ph.D. in Hydrology, Earth system science, Water resources engineering, Computational sciences, Computer sciences or a related field completed within the last 5 years (or expected soon).
• Demonstrated experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction.
• Strong background in computational sciences, including numerical methods, high-performance computing (HPC), or large-scale data analysis.
• Experience in applying AI/ML techniques to hydrological and Earth sciences.
• Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C++.
• Evidence of scholarly productivity, including peer-reviewed publications and conference presentations.
• Excellent written and oral communication skills and the ability to work effectively in a collaborative, multidisciplinary team environment.

Preferred Qualifications

• Knowledge of uncertainty quantification methods and causal inference for complex environmental systems.
• Experience with large-scale Earth system simulations, particularly using the Energy Exascale Earth System Model (E3SM).
• Background in coastal and compound flooding simulations, including subsurface–surface and hydrodynamic interactions.
• Demonstrated ability and strong motivation to conduct innovative, high-impact research and disseminate results through peer-reviewed publications and conference presentations.