This is an exciting opportunity for a highly motivated and integratively minded quantitative geographer or interdisciplinary scientist to join a rich and diverse applied research network, NSERC ResNet. Applications deadline is 30 June 2021.
ResNet’s primary research team of is currently involved in explorations of services in dykelands and wetlands such as pollination, carbon sequestration, wave attenuation, salt-water intrusion, food provision and cultural services. This post-doctoral fellow (PDF) will sit at the centre of the Bay of Fundy landscape team and be a primary liaison with the three overarching ResNet themes (governance, modelling and monitoring) and synthesis teams. The PDF will receive primary data and research findings from the larger team, and will integrate and translate those findings into a shared modelling space, with a range of other spatial datasets, to explore the tradeoffs involved (and for whom) in decisions such as dyke reinforcement, dyke removal/realignment and/or salt marsh restoration.
The end points of the landscape-scale models will not be as important as the process of building them: they will be used for conversation and collaborative learning rather than as legacy deliverables. To that end, the PDF will engage with landscape partners (e.g., NS Dept of Agriculture, AAFC, CBWES, Confederacy of Mainland Mi’kmaq) on land use scenarios; project-level partners (e.g. Statistics Canada, Apex RMS, Alces) on techniques of modelling and ecosystem accounting; and the other five ResNet landscapes as appropriate on shared ES issues. The PDF will also be expected to provide some project administrative support to the Bay of Fundy landscape case study co-leads (the co-supervisors) and play a lead role in liaison around the ResNet-level dashboards and data portals.
The position will be based at Dalhousie University’s School for Resource and Environmental Studies and be co-supervised by Dr. Kate Sherren and Dr. Jeremy Lundholm who is affiliated with CBWES Inc, TransCoastal Adaptations: Centre for Nature-based Solutions and the Biology Department of nearby Saint Mary’s University.
The successful candidate must have an integrative mindset and strong quantitative skills, able to perform data aggregation, synthesize primary research findings already published and underway, and build and work with landscape scenario models with the support of modelling and monitoring activities coordinated by other elements and partners of ResNet. The PDF must have strong analytical abilities; strong written and oral communication abilities in English; skill in data visualization and communication; an independent but collaborative work style; and competence communicating across difference (culture, discipline, etc). There will be scope for a motivated and self-driven PDF to transition from mentored research to an independent research agenda.