Oak Ridge National Laboratory
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Postdoctoral Research Associate in Coastal Landscape Geospatial Modeling and AI/ML Oak Ridge National Laboratory in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Postdoctoral appointment up to 24 months with potential for extension, subject to performance and availability of funding. ORNL offers competitive pay and benefits; relocation assistance is mentioned. No stipend amount is provided.
Country
United States
University
Oak Ridge National Laboratory

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About this position
Postdoctoral Research Associate – Coastal Landscape Geospatial Modeling at Oak Ridge National Laboratory (Oak Ridge, Tennessee, United States).
This postdoctoral opening is in the Plant-Soil Interactions Group within the Environmental Sciences Division at ORNL. The project focuses on coastal landscape analysis and modeling, especially the use of predictive machine learning, remote sensing, and ecological monitoring data to understand and forecast land loss and ecosystem transitions in wetland-rich landscapes of the Gulf Coast, with emphasis on coastal Louisiana.
The researcher will support the Exploring Gulf Region Ecosystem Transitions (EGRET) project, an interdisciplinary collaboration across DOE laboratories and universities. The work combines AI/ML, geospatial analysis, remote sensing, and process-based modeling to study disturbance-driven ecosystem transitions and plant–microbial–soil interactions across inundation and salinity gradients. The role involves synthesizing large environmental datasets, training multimodal and generative AI methods, and contributing to publications and conference presentations.
Eligibility highlights: applicants should have a PhD in Earth science, Ecology, Geography, Environmental Sciences, Geosciences, or a related field completed within the last 5 years or expected soon. Required experience includes AI/ML in an environmental or Earth science context, Python or related programming, and a strong research record. Preferred experience includes spatiotemporal or Earth system AI/ML, coastal landscape ecology, hydrology, geomorphology, and large heterogeneous environmental or remote-sensing datasets.
Funding and appointment: this is a postdoctoral appointment for up to 24 months, with possible extension depending on performance and funding availability. ORNL notes competitive pay and benefits, plus relocation assistance. No specific stipend amount is listed.
How to apply: submit your application through the ORNL jobs portal. Provide three letters of reference, either uploaded with the application or emailed to [email protected] with the position title and number in the subject line. For application issues, contact [email protected].
Funding details
Postdoctoral appointment up to 24 months with potential for extension, subject to performance and availability of funding. ORNL offers competitive pay and benefits; relocation assistance is mentioned. No stipend amount is provided.
What's required
A PhD in Earth science, Ecology, Geography, Environmental Sciences, Geosciences, or a related field completed within the last 5 years or expected soon; expertise in AI/ML methods in an environmental or Earth science context; experience programming in Python or related languages; a strong record of independent and productive research as shown by conference presentations and/or peer-reviewed publications; ability to work independently and collaboratively in a large multidisciplinary team; excellent interpersonal, oral, and written communication skills. Preferred: experience with AI/ML for spatiotemporal or Earth system data, including generative AI; expertise in coastal landscape ecology, hydrology, and/or geomorphology; experience analyzing large heterogeneous environmental, geospatial, remote-sensing, or time-series datasets.
How to apply
Apply through the ORNL jobs portal using the posting page. Submit your application materials and include three letters of reference; letters may be uploaded directly or emailed to [email protected] with the position title and number in the subject line. If you have trouble applying, contact [email protected].
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