Postdoctoral Research Scientist — Soil Biogeochemistry & ML

Montana State University; Bozeman (MT); Hybrid; USD 59,000 - 72,000; Full time

Job summary

Montana State University’s Department of Land Resources & Environ Sci seeks a PhD-level researcher to advance soil functional type frameworks and biogeochemical analyses under an EPSCoR project. You will work with Montana field campaigns, regional datasets, and national laboratories.

Responsibilities include developing ML-based SFTs, mapping if samples, and coordinating with PNNL and LLNL teams. Requires strong data skills, independent work style, and cross-disciplinary collaboration.

Qualifications

• 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.

Responsibilities

• Develop Soil Functional Type (SFT) framework using global observational soil data.
• Apply unsupervised machine learning to depth-explicit SFTs.
• Assess biogeochemical representativeness and map SFT; identify data gaps.
• Support Montana field sampling campaigns with analytical/logistical tasks.
• Integrate field data into SFT validation and coordinate benchmarking with collaborators.
• Explore Earth System Model structures and contribute to EMSL proposal development; participate in an extended residency at PNNL.

Skills

Machine learning

Statistical methods

R or Python

Environmental data

Scientific communication

Education

PhD in soil science or related field

Tools

R

Python

Job description

Montana State University’s Department of Land Resources & Environ Sci seeks a PhD-level researcher to advance soil functional type frameworks and biogeochemical analyses under an EPSCoR project. You will work with Montana field campaigns, regional datasets, and national laboratories.

Responsibilities include developing ML-based SFTs, mapping if samples, and coordinating with PNNL and LLNL teams. Requires strong data skills, independent work style, and cross-disciplinary collaboration.