Postdoctoral Research Scientist

About the role

We are seeking a postdoctoral researcher to join a newly funded DOE project, developing AI-driven “Soil Functional Type” (SFT) framework, which is a new way of classifying global soils by their biogeochemical function (mineralogy, depth profile, organic matter stabilization) rather than traditional taxonomy. The project is a partnership between MSU, Pacific Northwest National Laboratory (PNNL), and Lawrence Livermore National Laboratory (LLNL), and directly feeds into next-generation Earth System Model development (DOE’s E3SM/ELM). The postdoctoral researcher will lead key project objectives, including database harmonization, SFT mapping and validation, and ELM model–data benchmarking, in close collaboration with the PI and national laboratory partners.

The Department of Land Resources and Environmental Sciences (LRES) at Montana State University conducts research and teaching across soil science, ecology, hydrology, and natural resource management. This position supports a newly funded DOE Established Program to Stimulate Competitive Research (EPSCoR) project led by PI Dr. Sophie von Fromm, in partnership with Pacific Northwest National Laboratory and Lawrence Livermore National Laboratory.

Responsibilities

• Soil Functional Type (SFT) Framework Development: Compile and harmonize global observational soil databases in support of SFT framework development. Apply (unsupervised) machine learning methods to develop depth-explicit SFTs.
• Biogeochemical Representativeness Analysis & SFT Mapping: Evaluate whether existing global soil databases adequately sample the biogeochemical process space relevant to soil organic matter (SOM) dynamics. Produce global SFT maps and identify priority data/sampling gaps.
• Field Campaign Support: Provide analytical and logistical support for a Montana field sampling campaign. Integrate field-collected data (reactive mineralogy, radiocarbon, SOM fractionation) into SFT validation.
• ELM Benchmarking & PNNL Collaboration: Lead benchmarking activities in close collaboration with Dr. Avni Malhotra (PNNL). Diagnose where current Earth System Model structures fail to represent mineral–organic matter controls on carbon persistence. Complete an extended research residency at PNNL; contribute to development of an EMSL user proposal for molecular-scale characterization.

Requirements

• PhD in soil science, biogeochemistry, environmental data science, Earth system modeling, or a related field.
• Demonstrated experience applying statistical or machine learning methods to environmental or geospatial datasets.
• Proficiency in a scientific programming language (R or Python).
• Experience working with large environmental or soil datasets.
• Demonstrated experience with written and oral scientific communication.

Skills

• Experience with unsupervised machine learning methods.
• Experience with Earth System Models (e.g., E3SM/ELM) or land surface/biogeochemical modeling.
• Background in soil biogeochemistry, mineralogy, or soil organic matter dynamics.
• Prior experience collaborating with national laboratories or large multi-institutional research teams.
• Peer-reviewed publication record.

Pay

Salary $65,000 annually, commensurate with experience, education, and qualifications.

Benefits

Benefits eligible.