Requisition Id 15769 <div id="tor__flistingLayout_readOnlyDiv"> <div id="tor__flistingLayout_content"> Overview: 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). The successful candidate will have a strong background in computational science, data analysis, and process-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and data-model integration, leveraging the U.S. Department of Energy’s (DOE) Leadership-Class Computing Facilities to advance predictive understanding of complex environmental systems. Major Duties/Responsibilities: <ul style="list-style-type:disc"> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Contribute to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Publish research findings in high-impact journals and present results at national and international conferences.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Engage with collaborators across DOE laboratories, universities, and partner agencies to broaden the applications of AI-enabled hydrological modeling.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Ensure compliance with ORNL’s safety, security, quality, and environmental standards while carrying out all research activities. </li> </ul> Technical Questions: Please contact Dan Lu <a style="color:black" href="mailto:[email protected]">[email protected]</a> Basic Qualifications: <ul style="list-style-type:disc"> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">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).</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Demonstrated experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Strong background in computational sciences, including numerical methods, high-performance computing (HPC), or large-scale data analysis.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Experience in applying AI/ML techniques to hydrological and Earth sciences.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C++.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Evidence of scholarly productivity, including peer-reviewed publications and conference presentations.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Excellent written and oral communication skills and the ability to work effectively in a collaborative, multidisciplinary team environment. </li> </ul> Preferred Qualifications: <ul style="list-style-type:disc"> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Knowledge of uncertainty quantification methods and causal inference for complex environmental systems.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Experience with large-scale Earth system simulations, particularly using the Energy Exascale Earth System Model (E3SM).</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Background in coastal and compound flooding simulations, including subsurface–surface and hydrodynamic interactions.</li> <li style="font-family:arial, helvetica, sans-serif;font-size:10.0pt;color:black">Demonstrated ability and strong motivation to conduct innovative, high-impact research and disseminate results through peer-reviewed publications and conference presentations.</li> </ul> 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. The appointment length will be for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding. ORNL Ethics and Conduct: As a member of the ORNL scientific community, you will be expected to commit to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: <a style="color:black" href="https://www.ornl.gov/content/research-integrity">https://www.ornl.gov/content/research-integrity</a> Benefits at ORNL: UT Battelle offers an exceptional benefits package to include matching 401K, Pension Plan, Paid Vacation and Medical / Dental plan. Onsite amenities include Credit Union, Medical Clinic and free Fitness facilities. Relocation: UT Battelle offers a wide range of relocation benefits for individuals and families to make it easier to come and work here. If you are invited to interview, please ask your Recruiter about relocating with ORNL </div> </div> This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired. We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment. ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.

Postdoctoral Research Associate - AI for Hydrological Modeling (Oak Ridge, TN, US, 37830) @ Oak Ridge National Laboratory

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