| Location: | Durham |
|---|---|
| Salary: | £38,784 to £46,049 per annum Grade 7 |
| Hours: | Full Time |
| Contract Type: | Fixed-Term/Contract |
| Placed On: | 7th May 2026 |
|---|---|
| Closes: | 21st May 2026 |
| Job Ref: | 26000454 |
The Role
Applications are invited for a Postdoctoral Research Associate in Ecological Modelling and Land-Use Change, with a particular emphasis on linking high-resolution land-use data with population and community ecological dynamics.
The successful applicant will work on a project focused on developing high-resolution, policy-relevant projections of land-use change and their implications for biodiversity dynamics across spatial scales. The research will build on recent advances in global land-use harmonisation and downscaling (e.g. HILDA+, One Earth products), with the aim of integrating these datasets into ecological forecasting frameworks that explicitly represent population, metapopulation, and community processes.
A central aim of the project is to improve our ability to predict how species and ecological communities respond to interacting drivers of global change, including land-use change and climate change. This will involve combining large-scale environmental datasets with process-based or hybrid ecological models, and exploring emerging approaches to scaling such models, including the use of emulation and AI-assisted workflows.
The postholder will also contribute to international collaborative activities, including the Biodiversity Model Intercomparison Project (BMIP), supporting the implementation and execution of ecological models within intercomparison exercises and contributing to the synthesis of results across modelling approaches.
The postholder will work closely with Professor Justin Travis and collaborators to:
The role provides an excellent opportunity to contribute to cutting-edge research at the interface of ecological modelling, global change biology, and environmental data science, with strong links to international initiatives (e.g. GEO BON and biodiversity model intercomparison efforts).
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