Ecosystem Science Jobs (NOW HIRING)

Requisition Id 16704
Overview:
We are seeking a Postdoctoral Research Associate who will focus on AI-enabled plant ecophysiology to improve mechanistic understanding and predictions of ecosystem responses to environmental change. This position resides in the Ecosystem Processes Group in the Environmental Sciences Division at Oak Ridge National Laboratory (ORNL). The selected candidate will work with Dr. Jeffrey Warren and Dr. Lianhong Gu and collaborate with researchers in the ORNL Terrestrial Ecosystem Science (TES) Scientific Focus Area (SFA) to integrate experimental measurements and trait databases to assess ecosystem response to environmental forcing.
The primary research focus is AI-forward, experiment-driven. This position encourages leveraging AI/ML methods to assess plant physiological responses to current or imposed environmental conditions. Research may leverage:

• Laboratory, growth chamber and field experimental data, and/or new measurements to quantify molecular to ecosystem scale responses to warming, drought, and elevated CO2 (e.g., gas exchange, fluorescence, hydraulics, respiration, water potential, thermal tolerance)
• Trait synthesis at scale (e.g., using trait databases TRY, FRED, LeafWeb, Sapfluxnet, PSInet) to translate trait variation into model parameter priors and functional constraints, and to explore parameter relationships with environmental conditions
• Hybrid modeling that combines mechanistic ecophysiology with AI, such as:
  • Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships
  • Bayesian statistics and neural and Gaussian-process emulators for accelerating parameter estimation and uncertainty propagation
  • Selective cross-scale evaluation using complementary ecosystem observations (e.g., experiments) to test how AI-informed analyses can contribute to ecosystem-scale simulations.

Major Duties/Responsibilities:

• Conduct observational and manipulative ecophysiological research to quantify plant and ecosystem responses to abiotic stressors (e.g., heat and drought) and identify mechanistic resilience thresholds across the soil-plant-atmosphere continuum
• Lead soil-plant-atmosphere hydraulics measurements at the Missouri flux tower site (MOFLUX), including plant hydraulics, rooting depth, canopy temperature and other parameters for tree species that vary in sensitivity to drought. Scale water flux measurements to the site level for comparison with carbon/water exchange based on eddy flux measurements
• Use AI/ML data integration, modeling and trait databases to scale up ecophysiological mechanisms of ecosystem water and carbon flux from MOFLUX to broader region/ecotone/biome response to changes in seasonality of precipitation, temperature, and atmospheric constituents
• Contribute to other AI/ML synthesis activities - e.g., neutron imaging, SPRUCE hydraulic/thermal thresholds and scaling
• Produce and publish AI-ready datasets to the ESS-DIVE data archive and BER data lakehouse
• Develop AI pipelines for experimental ecophysiology, including automated QC and uncertainty-aware learning from sparse/noisy measurements
• Build hybrid mechanistic-AI models linking traits to photosynthesis, stomata, hydraulics, and respiration across experimental gradients
• Benchmark and stress-test model improvements against experimental datasets (e.g., SPRUCE, MOFLUX and related lab/field measurements), and publish open reproducible code and results
• 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.

Basic Qualifications:

• PhD (completed by start date) in plant ecophysiology, plant biology, ecology, Earth system science, or related field
• Strong understanding of plant physiological processes (photosynthesis, stomata, hydraulics, respiration, plant-water relations)
• Demonstrated strength in quantitative methods and programming (e.g., Python or R; reproducible workflows; version control)
• Experience or interest in AI/ML application to ecophysiological research

Preferred Qualifications:

• Hands-on experience with lab/growth chamber and/or field experimental ecophysiology measurements
• Experience applying ML/AI to biological or environmental data (e.g., transformer, state space models, multi-layer perceptrons, convolutional neural networks)
• Familiarity with trait databases (e.g., TRY, LeafWeb) and trait-based scaling
• Excellent written and oral communication skills
• 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.
Names and contact information for three professional references are required.
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.
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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.