Postdoctoral Research Associate in Grassland Ecosystem Modeling and Carbon Cycle Forecasting

University of Oklahoma; United States; On-site; USD 65,000 - 90,000; Full time

Job summary

University of Oklahoma invites applications for a Postdoctoral Research Associate in grassland ecosystem modeling, focusing on the Microbial-Enzyme Decomposition (MEND) model to predict soil carbon dynamics. The role includes data assimilation from diagnostic models and remote sensing to improve prognostic simulations.

The successful candidate will collaborate across ecology, remote sensing, and AI/ML to generate high-resolution carbon flux data products and contribute to publications and

Qualifications

• PhD in ecosystem ecology, Earth system science, biogeochemistry, soil science, environmental science, applied mathematics, computer science, or related field.
• Strong background in ecosystem modeling, terrestrial carbon-cycle modeling, soil carbon modeling, biogeochemical modeling, ecological forecasting, statistics, or machine learning.
• Demonstrated programming skills in Fortran, Python, R, Julia, MATLAB, or similar languages.
• Strong quantitative, writing, and presentation skills.
• A strong publication record or clear evidence of potential for peer-reviewed publications.

Responsibilities

• Develop, calibrate, and evaluate ecosystem models, especially MEND, for grassland carbon-cycle simulations.
• Use long-term experimental and observational data from Oklahoma grassland sites to improve model parameterization and evaluation.
• Assimilate data products from diagnostic models and remote sensing into prognostic model simulations.
• Generate geospatial data products of plant biomass, root biomass, and soil organic carbon stocks at high spatial and temporal resolution.
• Conduct scenario simulations to evaluate grassland carbon responses to climate variability, extreme events, altered precipitation intensity and frequency, and land-use change.
• Work collaboratively with an interdisciplinary team in ecosystem ecology, microbial ecology, remote sensing, AI/ML, biogeochemistry, and environmental modeling.
• Lead and contribute to peer-reviewed publications, presentations, data products, and proposal development.

Skills

Fortran

Python

R

Julia

MATLAB

Statistics

Machine learning

Scientific writing

Presentation skills

Education

PhD in ecosystem ecology or related field

Job description

University of Oklahoma Norman Campus: Dodge Family College of Arts and Sciences: Biological Sciences

Location

Institute for Environmental Genomics, School of Biological Sciences, University of Oklahoma, Norman Campus

Open Date

Aug 04, 2026

Deadline

Jun 04, 2027 at 11:59 PM Eastern Time

Description

The Institute for Environmental Genomics (IEG) and the School of Biological Sciences at the University of Oklahoma invite applications for a Postdoctoral Research Associate position in grassland ecosystem modeling, soil carbon dynamics, and ecological forecasting. The successful candidate will contribute to a funded project focused on improving prognostic ecosystem models and generating high-resolution data products of carbon fluxes and carbon stocks in grassland ecosystems. The position is closely aligned with the Center for Grassland Ecosystem Forecasting, which aims to integrate field observations, remote sensing, diagnotic and prognostic models, AI/ML approaches, digital twins, and decision-support tools to improve grassland management and conservation in Oklahoma and beyond.

The postdoc will focus on using and improve the Microbial-Enzyme Decomposition (MEND) model, which explicitly represents mircrobial processes and predicts plant growth and soil carbon dynamics. MEND includes soil organic matter partitioning, microbial physiological states, enzymatic decomposition, coupled carbon-nitrogen processes, inorganic nitrogen dynamics, the use of metagenomic data, and canopy photosynthesis as a dynamic carbon input to soil.

Major Responsibilties

• Develop, calibrate, and evaluate ecosystem models, especially MEND, for grassland carbon-cycle simulations.
• Use long-term experimental and observational data from Oklahoma grassland sites, including KAEFS and eddy covariance tower sites, to improve model parameterization and evaluate model performance.
• Assimilate data products from diagnostic models and remote sensing into prognostic model simulations.
• Generate geospatial data products of plant biomass, root biomass, and soil organic carbon stocks at high spatial and temporal resolution.
• Conduct scenario simulations to evaluate grassland carbon responses to climate variability, extreme events, altered precipitation intensity and frequency, and land-use change.
• Work collaboratively with an interdisciplinary team in ecosystem ecology, microbial ecology, remote sensing, AI/ML, biogeochemistry, and environmental modeling.
• Lead and contribute to peer-reviewed publications, presentations, data products, and proposal development.

Qualifications

Required Qualifications

• A Ph.D. in ecosystem ecology, Earth system science, biogeochemistry, soil science, environmental science, applied mathematics, computer science, or a closely related field.
• Strong background in ecosystem modeling, terrestrial carbon-cycle modeling, soil carbon modeling, biogeochemical modeling, ecological forecasting, statistics, or machine learning.
• Demonstrated programming skills in Fortran, Python, R, Julia, MATLAB, or similar languages.
• Strong quantitative, writing, and presentation skills.
• A strong publication record or clear evidence of potential for peer-reviewed publications.

Preferred Qualifications

Preferred candidates will have in one or more of the following areas:

• Process-based ecosystem modeling, especially microbial-explicit soil carbon models.
• Model-data integration, data assimilation, parameter estimation, uncertainty analysis, or Bayesian/statistical modeling.
• Grassland ecology, soil carbon dynamics, plant-soil-microbe interactions, or carbon-nitrogen cycling.
• Remote sensing data products, geospatial analysis, Google Earth Engine, or GIS.
• High-performance computing, reproducible workflows, or development of open-source modeling pipelines.
• Experience working with eddy covariance data, long-term ecological experiments, or large environmental datasets.

The University, in compliance with all applicable federal and state laws and regulations, does not discriminate on the basis of race, color, national origin, sex, sexual orientation, marital status, genetic information, gender identity/expression (consistent with applicable law), age (40 or older), religion, disability, political beliefs, or status as a veteran in any of its policies, practices, or procedures. This includes but is not limited to admissions, employment, housing, financial aid, and educational services.

Why You Belong at the University of Oklahoma

The University of Oklahoma values our community's unique talents, perspectives, and experiences. At OU, we aspire to harness our innovation, creativity, and collaboration for the advancement of people everywhere. You Belong Here!

Mission of the University of Oklahoma

The Mission of the University of Oklahoma is to provide the best possible educational experience for our students through excellence in teaching, research and creative activity, and service to the state and society.