Postdoctoral Research Scientist

Postdoctoral Research Scientist

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.

Position Number - 4C7717

Department - Land Resources & Environ Sci

Division - College of Agriculture/MAES

Appointment Type - Research Professional

Contract Term - Fiscal Year

Semester

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FLSA - Exempt

Union Affiliation - Exempt from Collective Bargaining

FTE - 1.0

Benefits Eligible - Eligible

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

Contract Type - LOA

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Recruitment Type - Open

General Statement - 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.

Duties and 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.

Required Qualifications – Experience, Education, Knowledge & Skills - 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.

Preferred Qualifications – Experience, Education, Knowledge & 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.

The Successful Candidate Will - Ability to work independently and manage multiple concurrent project objectives. Strong collaborator across disciplines, institutions, and remote research teams. Strong communication skills. Detail-oriented and highly organized, with strong data management practices. Adaptable to both computational/lab-based work and field-based research settings.

Position Special Requirements/Additional Information - Position is contingent upon continued DOE EPSCoR grant funding. Requires an extended research residency at Pacific Northwest National Laboratory (Richland, WA). Occasional travel to Lawrence Livermore National Laboratory and Montana field sites required. Occasional travel to national scientific conferences (e.g., AGU) required. This job description should not be construed as an exhaustive statement of duties, responsibilities or requirements, but a general description of the job. Nothing contained herein restricts Montana State University's rights to assign or reassign duties and responsibilities to this job at any time.

Physical Demands - Most work is computer-based. Field campaign support may require traversing uneven outdoor terrain, digging and soil sampling, and transporting up to 40 lbs, with exposure to variable weather conditions. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily with or without reasonable accommodations. The requirements listed above are representative of the knowledge, skill, and/or ability required.

This position has supervisory duties? - No

Posting Detail Information

Number of Vacancies - 1

Desired Start Date

Position End Date (if temporary)

Open Date

Close Date

Applications will be: Screening of applications will begin on October 7, 2026; however, applications will continue to be accepted until an adequate applicant pool has been established.

EEO Statement - Montana State University is an equal opportunity employer. MSU does not discriminate against any applicant on the basis of race, color, religion, creed, political ideas, sex, sexual orientation, gender identity or expression, age, marital status, national origin, physical or mental disability, or any other protected class status in violation of any applicable law. In compliance with the Montana Veteran's Employment Preference Act, MSU provides preference in employment to veterans, disabled veterans, and certain eligible relatives of veterans. To claim veteran's preference, please complete the veteran's preference information located in the Demographics section of your profile.