Postdoctoral Research Scientist

Announcement Number

Announcement Number STAFF - VA - 27112

Position Number

Position Number 4C7717

Department

Department Land Resources & Environ Sci

Division

Division College of Agriculture/MAES

Appointment Type

Appointment Type Research Professional

Contract Term

Contract Term Fiscal Year

Semester

If other, specify From date

If other, specify End date

FLSA

FLSA Exempt

Union Affiliation

Union Affiliation Exempt from Collective Bargaining

FTE

FTE 1.0

Benefits Eligible

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

Contract Type

Contract Type LOA

If other, please specify

Recruitment Type

Recruitment Type Open

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.

• 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.
• 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.

• 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.

• 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.

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

• 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…