Research Associate - Pest risk modeler
University of British Columbia
- Vancouver, BC
- 70.000 $ par an
- Permanent
- Temps-plein
- 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.
- 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.
- 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.
- A letter of application describing past achievements and future research interests;
- A detailed curriculum vitae;