Job DescriptionPosition Summary:One Ecologist/Forestry Researcher (Full-time, 40 hrs/week) position available with Colorado State University (CSU) Center for Environmental Management of Military Lands (CEMML). This position is part of CEMML's Early-Career Development Program, which helps emerging professionals bridge academic learning and career success through hands-on experience, mentorship, and professional development. To learn more visit: Early-Career Development Program - CEMML.
This is a research focus position. The role will use GIS and remotely sensed data, along with Forest Inventory and Analysis (FIA) data to characterize, map, model and analyze tree species composition and related forest attributes in a tropical environment. This will aid in understanding forest ecosystem disturbance and resilience.
For full consideration, please apply by Monday, November 23, 2026, 11:59 PM (MT). Essential Job Duties:- Combine existing spatial data with additional predictor variables developed as needed, with Forest Inventory and Analysis (FIA) data to characterize and map spatial gradients in tree species composition and related forest attributes.
- Evaluate whether mapped outputs or model derived spatial predictors can reduce uncertainties in FIA estimates of tree species basal area, biomass, or related composition metrics through one or more model assisted estimation approaches.
- Link mapped species composition outputs to an existing dataset of fire occurrences to characterize tree species composition where fires occurred, including areas where field surveys of tree species are not available.
- Develop a comparison of uncertainties in FIA estimates of species basal area, biomass, or related composition metrics when estimates are based only on FIA plots versus when model assisted estimation uses mapped auxiliary information.
- Create maps of model derived species composition gradients, or related composition outputs.
- Model derived composition values for places where fires occurred, along with related charts that help characterize the species composition of forests where fires occur.
Decision Making:- Determine and develop appropriate model parameters.
- Determine appropriate spatial predictor data sets.
Supervision:Conditions of Employment:- Successful completion of CSU's standard pre-employment background check.
Minimum Qualifications:- PhD in Systems Ecology or related field, including coursework in ecology, ecosystem climatology, statistics, spatial statistics, machine learning, applied bioinformatics, data visualization, geographic information systems and science communication.
- One year experience in systems ecology, including work in GIS and remote sensing and FIA plot data in tropical islands of the US.
- Experience as first author publishing a refereed journal article in a top journal focused on research typically requiring geospatial data and analysis (e.g., Remote Sensing or Ecological Indicators).
- Experience with programming languages including R and Python.
Preferred Qualifications:- Experience developing spatial or statistical models of forest composition, biomass, basal area, or related ecological attributes.
- Experience applying model-assisted estimation or other methods that integrate forest inventory data with auxiliary spatial information.
- Experience integrating remote sensing, climate, topographic, fire, or other disturbance data in geospatial analyses.
- Advanced proficiency using R, Python, and GIS for spatial modeling, uncertainty analysis, visualization, and reproducible workflows.
- Experience communicating geospatial research through peer-reviewed publications, technical reports, maps, or presentations.
Salary Range$78,000 to $84,000
Required Application DocumentsTo apply, please upload the following applicant documents. Ensure your materials fully address the required and preferred job qualifications of the position. Please note, applicants may redact information from their application materials that identifies their age, date of birth, or dates of attendance at or graduation from an educational institution.
Cover Letter, Resume/CV
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