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Postdoctoral Scholar: Spatial Analysis Of Forest Treatments

The Ecological Restoration Institute (ERI) at Northern Arizona University 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 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 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:

• 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 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.

Responsibilities include:

• 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 include:

• 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.

Preferred qualifications include:

• 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 include:

• Forest ecology, fuels management, and treatment effects.
• Remote sensing principles and spatial analysis.
• Model development, validation, and uncertainty assessment.
• Scientific programming and algorithm development.
• Spatial data processing and analysis of large datasets.
• Technical writing and documentation.
• Project organization and collaboration.
• Work independently while engaging effectively with collaborators.
• Translate ecological and management concepts into computational tools.
• Communicate complex methods and results clearly to diverse audiences.