Postdoctoral Scholar : Wildfire Analytics & Landscape Modeling

This position is part of the Post Doctoral Fellows Association and has an initial appointment of one year, with the possibility of extension.

This position has a comprehensive benefits package.

Location –   This role is in-person at North Campus, Edmonton.

Position Summary

The Wildfire Analytics lab is seeking multiple Postdoctoral Scholars to join our rapidly expanding research program. We’re an interdisciplinary team of researchers from varied backgrounds creating innovative, accessible, practical tools and methods for decision makers working to ensure that human and natural systems thrive in fire prone environments. Details about our team and projects are available on our website wildfireanalytics.org.

We are recruiting Postdoctoral Scholars who will contribute to one or more of the following research themes:

Theme 1: Novel enhancements and applications of our wildfire exposure and directional vulnerability assessment methods. These assessments are now well established and are being used across Canada and internationally in Australia, Portugal and Alaska. Recent applications of these methods have addressed fuel treatment optimization and assessment of evacuation and telecommunications vulnerabilities. We are currently exploring applications of these methods for informing powerline fire risk management. This year, we are launching a new study to explore applications of exposure and directional vulnerability assessments in the far north for informing community protection planning for dozens of small, isolated communities. Each new use-case generates opportunities for customization, enhancement and development of novel assessment metrics and approaches. Postdoctoral Scholars are needed to identify, investigate, and develop functional enhancements to our current methods based on these varied use-cases and applications.

Theme 2: Integration of ignition and weather with exposure and directional vulnerability assessments. Exposure and directional vulnerability are strictly fuel-based assessments that were originally designed to inform strategic planning horizons (i.e., the next or several years). We deliberately decoupled our assessments from variable factors like ignitions or weather, to focus on the fuel hazard, which is the only aspect of the fire environment that remains somewhat static over a strategic planning horizon and can be actively managed. But our fuel-based assessments can be interpreted over much shorter daily horizons during which highly variable factors like ignitions and fire weather are known. Fire management agencies have started doing just that: using exposure assessments to inform daily planning and suppression priorities, for example by intersecting current or projected fire perimeters with viable fire pathways into communities mapped with our directional vulnerability assessments in order to prioritize suppression action. Postdoctoral scholars are needed to develop new methods for integrating temporally variable information (ignition and weather) with our fuel based exposure and directional assessments for operational decision support and predictive modeling.

Theme 3: Influence of natural barriers and anthropogenically modified landcover on wildfire dynamics at multiple spatial scales. We demonstrated that exposure assessments can be used to identify priority or optimal locations for interventions like fuel reduction treatments, but best practices for actually implementing these interventions on the ground are not well defined. We are therefore expanding our efforts to assess the influence of natural barriers and anthropogenically modified land cover on wildfire dynamics at multiple spatial scales to inform fuel management intervention prescriptions and design criteria. These efforts include investigations to inform strategic planning (i.e., fuel treatment priorities, prescribed fire and harvest sequencing, location of strategic containment and ignition lines) as well as specific design criteria for a given intervention. To that end, we want to understand how non-fuel barriers (i.e. water bodies, burned areas) and fuel managed areas at stand and landscape scales influence fire behaviour and fire growth. We have been exploring empirical observations of wildfire interactions with features like harvest cut blocks and lakes; and investigating fuel structure and potential fire behaviour in fuel managed stands. Postdoctoral scholars are needed develop and apply advanced landscape modeling and predictive modeling methods in support of this work.

• Gathering and processing large-scale spatial datasets from various sources, including remote sensing (satellite imagery, LiDAR), field work (from UAVs or ground surveys), and existing databases.
• Constructing, adapting, and validating landscape models (e.g., wildfire risk, disturbance dynamics and interactions) using programming languages like R or Python and GIS software (ArcGIS, QGIS).
• Conducting scenario analysis to explore possible future states to uncover system vulnerabilities based on various policy, economic, or climate change scenarios to project future landscape dynamics or evaluate potential management strategies.
• Preparing research findings for dissemination, which includes drafting reports, writing high-impact academic publications, and presenting at national and international conferences.
• Working effectively within interdisciplinary teams, potentially leading specific project tasks, assisting with project management, and liaising with external partners and project participants.
• Assisting with supervision of undergraduate, MSc and PhD students; contribute to teaching, and help organize workshops or training sessions.
• Assisting in the preparation of research proposals and grant applications to secure future funding for research projects.

• The successful candidate must have a PhD awarded by the start date of this appointment in ecology, geography, remote sensing, or other related field with a proven background in geospatial analysis of broad-scale environmental processes, or environmental data science.
• Demonstrated skill with scripting geospatial processing in Python and/or R
• Experience with spatial data analysis and landscape modeling
• Experience with machine learning algorithms and advanced statistical techniques.
• Working knowledge of Artificial Intelligence, especially as it applies to geospatial analysis
• Experience with data management and analysis of large datasets
• An established record of peer-reviewed publication
• Demonstrated problem-solving and critical thinking skills
• Excellent interpersonal skills, work ethic and initiative
• Strong written and verbal communication skills

Preferred Qualifications

• Multi-disciplinary background and experience with or interest in natural resources, especially forest management or fire management
• Familiarity with wildland fire risk assessment methods
• Familiarity with fire behaviour prediction, fuel classification and fuel measurement
• Experience with spatial connectivity modeling
• Experience developing application programming interfaces (R packages, Shiny Apps, etc.)
• Interest in working on applied research topics of relevance to managers
• Experience communicating research findings to non-technical audiences

Application Instructions

Click “Apply Now” to submit the following:

• Resume ( Curriculum Vitae)
• Cover Letter – Include a brief summary of your research interests in your cover letter
• List of Publications