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

Requisition Id 17066 

Overview:

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.

Major Duties/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.

Basic Qualifications:

• 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.

Preferred Qualifications:

• 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

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.

Letters of Reference: Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to

• Instructions to upload documents to your candidate profile:
• Login to your account via jobs.ornl.gov
• View Profile
• Under the My Documents section, select Add a Document

Technical Questions:

Please contact Dan Lu: lud1@ornl.gov; Daryl Yang: yangd@ornl.gov

Project Questions:

Please contact Elizabeth Herndon: herndonem@ornl.gov; Benjamin Sulman: sulmanbn@ornl.gov

Security, Credentialing, and Eligibility Requirements: For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment. 

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 valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.

Other benefits include the following: 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, and Employee Discounts.

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.

If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.

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.