Job Description

Academic
Job Category Faculty Non Bargaining
Job Title Research Associate - Pest risk modeler
Department Research | Carroll Lab | Department of Forest and Conservation Sciences | Faculty of Forestry (Allan Carroll)
Posting End Date September 25, 2025
Note: Applications will be accepted until 11:59 PM on the Posting End Date.
Job End Date September 30, 2028
The expected pay for this position is $70,000/year.
The expected pay range for this position is approximately $70,000/year.
JOB SUMMARY
The Research Associate will lead the development and implementation of advanced species distribution and pest risk models, manage ArcGIS-based platforms for stakeholder engagement, and secure funding to advance innovative research on invasive forest and agricultural pests, delivering actionable decision-support tools for integrated pest management.
ORGANIZATIONAL STATUS
The position will be integrated into the Forest Insect Disturbance Ecology Lab (FIDEL) within the Dept. of Forest and Conservation Sciences, Faculty of Forestry. FIDEL is a research group that seeks to elucidate the independent and interacting effects of climate change and resource management on the irruptive dynamics and impacts of insect pests. Within FIDEL, the position will conduct original research that quantifies the risk and potential spread of irruptive insects in managed and unmanaged landscapes. The position will also provide mentorship in ecological modeling, entomology, and geospatial analysis to graduate students and post-doctoral fellows. More broadly, the position will provide advice to other research groups within the Dept. of Forest and Conservation Sciences regarding ecological modeling and geospatial analysis, and manage ArcGIS-based platforms to promote collaborative research opportunities. The position will work in collaboration with, and report directly to, the Principal Investigator (PI) of FIDEL.
WORK PERFORMED
The position will perform the following duties:

• Design and implement advanced species distribution models (SDMs) and dynamic spread models for invasive forest and agricultural pests, integrating multi-source ecological data using machine learning and statistical approaches.
• Develop and validate phenological models to forecast pest population dynamics and risk under varying climatic and biotic conditions.
• Deliver predictive risk assessment tools to support integrated pest management (IPM) strategies for stakeholders, including Agriculture and Agri-Food Canada (AAFC) and industry partners.
• Oversee the development, maintenance, and enhancement of ArcGIS Hub and Survey123 platforms for real-time data visualization, storage, and dissemination of pest risk information.
• Create interactive dashboards and decision-support tools to facilitate stakeholder access to pest risk forecasts and management recommendations.
• Ensure platform accessibility and usability for diverse end-users, including farmers, forest managers, and policymakers.
• Manage project budgets and resources, ensuring timely delivery of project milestones and deliverables as outlined in funded proposals.
• Provide mentorship and supervision to graduate students and early-career researchers in ecological modeling, entomology, and geospatial analysis.
• Foster a collaborative and inclusive research environment, guiding team members in the application of machine learning, R programming, and ArcGIS tools for pest management research.
• Coordinate with AAFC, Canadian Food Inspection Agency (CFIA), academic institutions, and industry partners to align research objectives with stakeholder needs.
• Facilitate knowledge transfer through workshops, webinars, and technical reports to communicate research findings to diverse audiences.
• Author and co-author peer-reviewed publications in high-impact journals to advance knowledge in invasive species and pest risk assessment.
• Present research at international conferences to enhance the project's visibility and impact.
• Integrate EDI principles into research activities, ensuring diverse perspectives are considered in stakeholder engagement and tool development.
• Participate in training and initiatives to enhance awareness and skills related to equity, diversity, and inclusion in academic and applied research settings.

MINIMUM QUALIFICATIONS
Qualifications for this position are as follows:

• PhD in Forest Ecology, Entomology, or a closely related field, with a focus on geospatial modeling, invasive species dynamics, and applied machine learning for pest risk assessment.
• Demonstrated expertise in developing and implementing species distribution models (SDMs) and dynamic spread models using machine learning and statistical approaches, specifically for agricultural or forest insect pests.
• Extensive experience in designing and managing ArcGIS-based platforms, including ArcGIS Hub and Survey123, for data visualization, storage, and stakeholder engagement in pest management contexts.
• Proven track record of securing competitive research funding as a principal or co-investigator, related to pest risk modeling or invasive species management.
• Minimum of 15 peer-reviewed publications in high-impact journals on topics related to invasive species, pest risk modeling, or geospatial analysis.
• Experience mentoring graduate students in ecological modeling or entomology, with a focus on invasive pest dynamics.
• Proficiency in R programming for phenological modeling and integration of biotic and abiotic predictors in pest risk assessments.
• Demonstrated ability to collaborate with interdisciplinary research teams, including Agriculture and Agri-Food Canada (AAFC) and academic institutions, on projects related to integrated pest management (IPM).
• Willingness to respect diverse perspectives, including perspectives in conflict with one's own.
• Demonstrates a commitment to enhancing one's own awareness, knowledge, and skills related to equity, diversity, and inclusion.

PREFERRED QUALIFICATIONS
Preferred qualifications for this position are as follows:

• Advanced expertise in integrating multi-source ecological data into species distribution models (SDMs) and pest risk assessments, with a focus on invasive forest and agricultural pests.
• Experience developing decision-support tools for pest management, such as interactive dashboards or risk forecasting systems, using ArcGIS Enterprise or similar geospatial platforms.
• Proficiency in machine learning frameworks for predictive modeling of pest population dynamics or spread, with a focus on integrating phenological and environmental predictors.
• Demonstrated success in leading interdisciplinary research projects, including coordination with government agencies and industry stakeholders in pest management.
• Strong record of science communication, including presenting research findings at international conferences and publishing in open-access journals to maximize outreach to diverse stakeholders.
• Experience in developing and delivering training workshops or webinars for end-users on pest risk assessment tools and integrated pest management (IPM) strategies.
• Familiarity with high-performance computing environments for processing large-scale geospatial and climatic datasets, such as those used in pest spread simulations.
• Proven ability to secure external funding from diverse sources to support innovative research in invasive species or ecological modeling.
• Strong interpersonal and leadership skills, with experience fostering collaborative research environments and mentoring early-career researchers or students in ecological or entomological research.
• Commitment to advancing equity, diversity, and inclusion in research, including experience working with diverse communities or stakeholders in the context of environmental or agricultural research.

APPLICATION PROCEDURE
An application package should include:

• A letter of application describing past achievements and future research interests;
• A detailed curriculum vitae;

Applications should be submitted online at .