Postdoctoral Peatland Modeling & AI for Ecosystem Dynamics

UT-Battelle; Oak Ridge (TN); On-site; USD 65,000 - 85,000; Full time

Job summary

UT-Battelle’s ORNL invites a Postdoctoral Research Associate to join the Earth Systems Modeling Group, focusing on the terrestrial ecosystem component of the Energy Exascale Earth System Model. You will advance nutrient cycling representations, work with field data and AI/ML tools, and collaborate across DOE networks to benchmark and improve model predictions.

The role offers opportunities at the interface of model development, observational synthesis, and emerging data-science methods, with

Qualifications

• A Ph.D. in Earth sciences, environmental sciences, atmospheric sciences, or a closely related field, either completed or near completion.
• Experience in ecological modelling and/or Land Surface Modelling.
• Knowledge and experience working in HPC environments.
• Programming experience in Fortran, Python or R.
• Experience handling and analyzing large observation data or model outputs.
• Knowledge of terrestrial biogeochemistry and nutrient-cycling processes.
• Strong communication skills.

Responsibilities

• Develop, implement, and test nutrient cycling representations within ELM and ELM-Peatlands.
• Evaluate model performance against SPRUCE, AmeriFlux, and other data.
• Compile and synthesize multi-source observational datasets for benchmarking.
• Explore AI/ML approaches to improve nutrient cycling and reduce uncertainty.
• Collaborate with ecologists, biogeochemists, modelers, and data scientists.
• Present findings at conferences and prepare manuscripts.

Skills

Ecological modelling

Land Surface Modelling

HPC experience

Fortran/Python/R

Large datasets

Terrestrial biogeochemistry

Communication skills

Education

Ph.D. in Earth/Environmental/Atmospheric sciences or related

Job description

UT-Battelle’s ORNL invites a Postdoctoral Research Associate to join the Earth Systems Modeling Group, focusing on the terrestrial ecosystem component of the Energy Exascale Earth System Model. You will advance nutrient cycling representations, work with field data and AI/ML tools, and collaborate across DOE networks to benchmark and improve model predictions.

The role offers opportunities at the interface of model development, observational synthesis, and emerging data-science methods, with