Postdoctoral Research Associate in Grassland Ecosystem Modeling and Carbon Cycle Forecasting

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.

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.

• 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.

• 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 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.