Postdoctoral Research Associate

**Job Description**
The Computational Hydrology and Atmospheric Science (CHAS) Group at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral Research Associate. This role focuses on developing and applying AI foundation models for hydrological and Earth system modeling, with an emphasis on improving predictive capabilities for compound flooding in coastal regions. The successful candidate will design and implement physics-informed and physics-ML hybrid approaches, conduct multimodal and multiscale data analysis, and collaborate with a multidisciplinary team leveraging leadership-class computing resources to advance predictive understanding of complex environmental systems.

**Skills & Abilities**
• Strong background in computational science
• Data analysis
• Process-based modeling of hydrological and Earth system processes
• Numerical methods
• High-performance computing (HPC)
• Large-scale data analysis
• Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C++
• Excellent written and oral communication skills
• Ability to work effectively in a collaborative, multidisciplinary team environment
• Knowledge of uncertainty quantification methods and causal inference for complex environmental systems (Preferred)
• Demonstrated ability and strong motivation to conduct innovative, high-impact research and disseminate results through peer-reviewed publications and conference presentations (Preferred)

**Qualifications**
Required Degree(s) in:
• Hydrology
• Earth system science
• Water resources engineering
• Computational sciences
• Computer sciences
• Related field

**Experience**
Other:
• Ph.D. 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.
• Evidence of scholarly productivity, including peer-reviewed publications and conference presentations.
• Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment.
• Experience with large-scale Earth system simulations, particularly using the Energy Exascale Earth System Model (E3SM) (Preferred)
• Background in coastal and compound flooding simulations, including subsurface-surface and hydrodynamic interactions (Preferred)

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