Postdoctoral Research Associate - AI/ML for Gulf Coast Ecosystem Dynamics

Oak Ridge National Laboratory • Oak Ridge, TN

3d • Onsite

About The Position

The Computational Hydrology and Atmospheric Science (CHAS) Group within the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral Researcher with expertise in artificial intelligence and machine learning (AI/ML), remote sensing, Earth and environmental sciences, and the analysis of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and recovery following hurricanes and other disturbances.

The successful 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 United States Gulf Coast. EGRET employs an integrated model–experiment (ModEx) approach accelerated by 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 at scale. 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.

Requirements

• A Ph.D. in Earth and Environmental Sciences, Hydrology, Computational Sciences, Remote Sensing, Data Science, or a related field, completed within the last 5 years or expected to be completed soon.
• Demonstrated experience developing or applying AI/ML approaches to Earth, environmental, ecological, hydrological, or geospatial problems.
• Experience with programming in Python, GEE, or R, and working with modern scientific computing and/or AI/ML frameworks.
• Experience with analyzing large, heterogeneous environmental, geospatial, remote-sensing, or time-series datasets.
• A strong record of scholarly productivity, as demonstrated by peer-reviewed publications and/or presentations at scientific conferences.
• Excellent written and oral communication skills and the ability to work effectively in a collaborative, multidisciplinary team environment.

Nice To Haves

• Experience with AI/ML approaches for spatiotemporal or Earth system data, including transformers, multimodal learning, representation learning, or related architectures.
• Experience working with satellite remote-sensing data such as Landsat, Sentinel, MODIS, SAR, LiDAR, or derived land-cover and vegetation product, and experience with in-the-cloud image processing
• Experience with geospatial foundation models or pretrained Earth-observation models.
• Experience with explainable AI, feature attribution, dimensionality reduction, clustering, representation analysis, or related approaches for extracting scientific understanding from AI/ML models.
• Demonstrated experience conducting interdisciplinary, systems-level research that integrates AI/ML with Earth and environmental science.
• Experience with coastal, wetland, or ecosystem ecology is preferred

Responsibilities

• Develop and apply AI/ML methods to integrate heterogeneous geospatial, remote-sensing, hydrological, meteorological, and environmental datasets across the Gulf Coast.
• Intergrade remote sensing approaches and AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and recovery following hurricanes and other disturbances at scale.
• Apply explainable AI and statistical methods to identify physical, environmental, and biological drivers of ecosystem change and resilience, quantify nonlinear interactions, and advance scientific understanding of coastal ecosystem resilience.
• Curate and develop reproducible AI-ready datasets, computational workflows, and research software for collaborative use within the EGRET project.
• Work closely with remote-sensing scientists, hydrologists, environmental scientists, process-based modelers, and computational scientists across DOE laboratories and universities to address project objectives.
• Present research results within the project and at national and international conferences, and publish findings in peer-reviewed journals, and datasets in DOE data repositories.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service.

Benefits

• medical and retirement plans
• flexible work hours
• on-site fitness
• banking
• cafeteria facilities
• Prescription Drug Plan
• Dental Plan
• Vision Plan
• 401(k) Retirement Plan
• Contributory Pension Plan
• Life Insurance
• Disability Benefits
• Generous Vacation and Holidays
• Parental Leave
• Legal Insurance with Identity Theft Protection
• Employee Assistance Plan
• Flexible Spending Accounts
• Health Savings Accounts
• Wellness Programs
• Educational Assistance
• Relocation Assistance
• Employee Discounts

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree