Postdoctoral Research Associate - Coastal Landscape Geospatial Modeling

Overview

The Plant-Soil Interactions Group within the Environmental Sciences Division (ESD) at Oak Ridge National Laboratory (ORNL) seeks a postdoctoral researcher in Coastal Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring data and develop data-driven methods for predicting land loss and ecosystem transitions in wetland-rich landscapes of the Gulf Coast with a focus on coastal Louisiana.

This candidate will directly support the Exploring Gulf Region Ecosystem Transitions (EGRET) project, an interdisciplinary and multi-institutional collaboration focused on disturbance-driven ecosystem transitions and their impacts across the U.S. Gulf Coast. EGRET employs an integrated model–experiment (Mod Ex) approach accelerated by artificial intelligence (AI) to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity gradients to shape ecological, hydrological, and geomorphological responses to abrupt disturbance.

A cohort of postdoctoral researchers will be hired across multiple institutions to collaboratively support scientific advances guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques.

• Synthesize, harmonize, and analyze large environmental monitoring and remote sensing datasets from coastal Louisiana
• Train and apply multimodal and generative AI methods to predict land loss and vegetation change at high spatial resolution
• Work closely with remote-sensing scientists, modelers, and empiricists across DOE laboratories and universities to address project objectives.
• Present results within the research group and at national and international conferences and publish manuscripts in peer-reviewed journals.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service.

• A PhD in Earth science, Ecology, Geography, Environmental Sciences, Geosciences, or a related field completed within the last 5 years or expected to be completed 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 indicated by conference presentations and/or publications in peer-reviewed journals.
• Ability to work independently and collaboratively as part of a large multidisciplinary team.
• Excellent interpersonal, oral, and written communication skills.

• Experience with AI/ML approaches for spatiotemporal or Earth system data, including generative AI methods.
• Expertise in coastal landscape ecology, hydrology, and/or geomorphology.
• Experience analyzing large, heterogeneous environmental, geospatial, remote-sensing, or time-series datasets.

Contact for Position:
Ben Sulman (sulmanbn)

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.

About ORNL:
As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are…