Job Type: On-site, postdoc
Location: Northern Arizona University - Flagstaff, Arizona
Pay: $58,882/year
Application Deadline: April 20, 2026
Description:
This position is an on-site position which requires the incumbent to complete their work primarily at an NAU site, campus, or facility with or without accommodation. Opportunities for remote work are rare.
This position is subject to the availability of funding. The incumbent is not eligible for Service Professional non-renewal notice, or Classified Staff layoff or recall status.
Driving a vehicle on behalf of the university is anticipated to be a regular part of this position. Arizona Administrative Code Fleet Safety Policy requires all employees who drive on university business become authorized by submitting Driver’s license information for driving record monitoring, and completion of training appropriate to the level of driving performed. The law applies to all faculty, staff, and students who drive personal or university-owned motorized vehicles for any business purpose. More information on the NAU Authorized Driver Policy can be found on the NAU website.
The Ecological Restoration Institute (ERI) at Northern Arizona University serves diverse audiences with objective science and implementation strategies that support ecological restoration and climate adaptation on Western forest landscapes. The ERI is nationally recognized as a leader in primary and secondary ecological and social science, scholarship, information exchange, collaborative efforts, and policy analysis supporting forest restoration and fire use. The ERI is one of three congressionally designated institutes, the Southwest Ecological Restoration Institutes (SWERI) located in Arizona, Colorado, and New Mexico, focused on bridging science and management to restore western fire-adapted forests.
The Ecological Restoration Institute seeks a Postdoctoral Scholar to develop and analyze spatial datasets across diverse forest types in the interior western United States. This position will focus on leveraging the ReSHAPE TWIG dataset - https://reshapewildfire.org/twig; an unprecedented, multi regional compilation of management records - to generate spatially explicit maps of forest-fuel treatment effects, build predictive models that estimate how long treatments remain effective under varying environmental conditions, and explore additional topics of interest.
The Postdoctoral Scholar will be expected to lead primary research efforts and work closely with agency partners and a large research team to support operational planning and decision support tools for restoration and wildfire risk mitigation. The position is designed as a full time, mentored research appointment that prepares the scholar to become an independent scientist at the interface of forest ecology, spatial analysis, and quantitative modeling.
The central objective is to develop actionable research products such as the following:
Use the ReSHAPE TWIG dataset to derive spatially explicit metrics of treatment intensity and heterogeneity across treatment types and forest conditions.
Integrate multi sensor remote sensing products (e.g., LiDAR, Landsat, Sentinel-1, Sentinel 2) to quantify structural and compositional change following treatment.Develop predictive models of treatment longevity that incorporate climate, vegetation, disturbance history, and treatment characteristics.
Produce spatially explicit outputs that inform landscape scale planning, fuels management, and long term restoration strategy.
The resulting products will directly support agencies, practitioners, and researchers seeking to understand how treatments perform over time and where future investments are most likely to be effective.
Responsibilities:
60% - Research and Tool Development
Develop reproducible and scalable workflows to map treatment intensity using remote sensing data and the ReSHAPE TWIG dataset.
Build predictive models of treatment longevity using statistical, machine learning, or process based approaches.
Create reproducible data workflows suitable for long term research and operational use.
Integrate spatial outputs with treatment records, ecological datasets, and landscape scale planning tools.
20% - Dissemination and Scholarly Output
Publish peer reviewed manuscripts describing methods, findings, and applications.
Develop technical documentation, example workflows, and open source repositories to support reuse.
Present results at scientific conferences, workshops, and partner briefings.
10% - Analysis, Validation, and Synthesis
Evaluate model performance over time and across forest types, treatment categories, and environmental gradients.
Assess uncertainty and sensitivity in treatment intensity mapping and longevity predictions.
Compare modeled outcomes with field data, monitoring records, or literature based expectations.
10% - Mentoring and Collaboration
Collaborate with faculty, research staff, and graduate students across forestry, ecology, and remote sensing disciplines.
Provide mentorship or technical guidance to students or staff working on related projects.
Minimum Qualifications:
PhD in forestry, remote sensing, ecology, geography, natural resources, or a closely related field.
Demonstrated experience analyzing spatial datasets related to vegetation.
Quantitative and programming skills (e.g., R, Python, or similar).
Demonstrated ability to design, document, and maintain reproducible research workflows.
*A combination of related education, experience, and training may be used as an equivalent to the above Minimum Qualifications.
Preferred Qualifications:
Experience with LiDAR, multispectral, and/or time series remote sensing datasets.
Familiarity with forest structure metrics and measurements.
Experience with cloud computing environments such as Google Earth Engine.
A record of peer reviewed scientific publication.
Knowledge of western U.S. forest ecosystems and forest management practices.
Knowledge, Skills, and Abilities:
Knowledge
Forest ecology, fuels management, and treatment effects.
Remote sensing principles and spatial analysis.
Model development, validation, and uncertainty assessment.
Skills
Scientific programming and algorithm development.
Spatial data processing and analysis of large datasets.
Technical writing and documentation.
Project organization and collaboration.
Abilities
Work independently while engaging effectively with collaborators.
Translate ecological and management concepts into computational tools.
Communicate complex methods and results clearly to diverse audiences.
