Description

­­Overview:   

The Watershed Systems Modeling Group (WSMG) within the Environmental Sciences Division (ESD) at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral Research Associate in the areas Artificial Intelligence (AI) for Integrated Hydrology Modeling. The successful candidate will have a strong background in computational science, data analysis, and process-based modeling of hydrologic or land surface processes. The WSMG group develops advanced surface/subsurface integrated hydrologic and reactive transport models, works with other groups to compare model and observations, and uses that modeling capability to advance predictive understanding of complex environmental systems. 

ESD is an interdisciplinary research and development organization with more than 60 years of achievement in local, regional, national, and international environmental research. Our vision is to expand scientific knowledge and develop innovative strategies and technologies that will strengthen the nation’s leadership in creating solutions to help sustain the Earth’s natural resources. Our scientists conduct research, develop technology, and perform analyses to understand and assess responses of environmental systems at the environment-human interface and the consequences of alternative energy and environmental strategies. 

Please contact Dr. Scott Painter (paintersl@ornl.gov) with questions related to this position.

Major Duties/Responsibilities:  

        Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. 

        Design and implement hybrid approaches that integrate process-based simulations with data-driven methods to advance hydrologic process understanding and prediction.

        Integrate 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 system scientists, and computational scientists for large-scale model training, testing, and deployment.

        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.

        Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. [KC1] 

Basic Qualifications:

        A Ph.D. in Hydrology, Civil/environmental engineering, Earth system science, Water resources engineering, Computational sciences or a related field completed within the last 5 years (or expected soon)

        Demonstrated experience in hydrologic or land surface modeling. 

        Experience in applying AI/ML techniques to hydrologic or 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:

        Experience with spatially explicit integrated surface/subsurface hydrologic models. 

        Experience with reactive transport or thermal hydrology modeling. 

        Experience with high-performance computing (HPC).

        Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.

        Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Special Requirements: 

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 up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding. 

Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to Postdocrecruitment@ornl.gov with the position title and number referenced in the subject line.[PK2] 

Instructions to upload documents to your candidate profile:

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        Under the My Documents section, select Add a Document