Montana State University
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Postdoctoral Research Scientist in Soil Functional Type Framework, Biogeochemistry, and Earth System Modeling Montana State University in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Postdoctoral research position funded by a DOE EPSCoR grant. Salary is $65,000 annually, commensurate with experience, education, and qualifications. Position is contingent upon continued DOE EPSCoR grant funding.
Deadline
Oct 7, 2026
Country
United States
University
Montana State University
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About this position
Montana State University is advertising a Postdoctoral Research Scientist in soil science, biogeochemistry, environmental data science, Earth system modeling, and machine learning. The project develops an AI-driven Soil Functional Type (SFT) framework to classify global soils by biogeochemical function rather than traditional taxonomy, with applications to global soil databases, SFT mapping and validation, and Earth system model benchmarking.
The postdoctoral researcher will work in the Department of Land Resources & Environmental Sciences (College of Agriculture/MAES) and collaborate with Pacific Northwest National Laboratory and Lawrence Livermore National Laboratory. Key duties include harmonizing observational soil datasets, applying unsupervised machine learning to depth-explicit SFTs, evaluating representativeness of global soil databases, producing global SFT maps, supporting a Montana field sampling campaign, and contributing to ELM/E3SM benchmarking and model–data comparisons.
Eligibility highlights: PhD in soil science, biogeochemistry, environmental data science, Earth system modeling, or a related field; experience with statistical or machine learning methods on environmental/geospatial datasets; proficiency in R or Python; experience with large environmental or soil datasets; and strong scientific communication skills. Preferred experience includes unsupervised ML, Earth System Models (E3SM/ELM), soil biogeochemistry/mineralogy, national lab collaboration, and publication record.
Funding: the role is supported by a newly funded DOE EPSCoR project and offers a salary of $65,000 annually (commensurate with experience, education, and qualifications). The position is contingent on continued grant funding.
Location: Bozeman, Montana, United States, with an extended research residency at PNNL in Richland, Washington, plus occasional travel to LLNL, Montana field sites, and conferences.
How to apply: submit a CV, a 1-page research/interest statement, and up to 5 selected publications with a one-sentence explanation for each. Screening begins on 2026-10-07, and applications remain open until a suitable applicant pool is established.
Funding details
Postdoctoral research position funded by a DOE EPSCoR grant. Salary is $65,000 annually, commensurate with experience, education, and qualifications. Position is contingent upon continued DOE EPSCoR grant funding.
What's required
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 R or Python. Experience working with large environmental or soil datasets. Demonstrated written and oral scientific communication. Preferred: unsupervised machine learning, Earth System Models (e.g., E3SM/ELM) or land surface/biogeochemical modeling, soil biogeochemistry/mineralogy/soil organic matter dynamics, collaboration with national laboratories or large multi-institutional teams, and a peer-reviewed publication record. Position is not eligible for new sponsorship.
How to apply
Submit a CV, a 1-page statement of interest/research statement, and a list of up to 5 selected publications with a one-sentence explanation for each. Apply through the Montana State University jobs portal using the provided application link. Screening begins on October 7, 2026 and applications are accepted until an adequate pool is established.
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