Research Associate (Statistical Population Ecology)

We are seeking a highly motivated and quantitatively skilled Research Associate to join an exciting NERC-funded project, “Harnessing Ensemble Models for Robust Near-Term Population Forecasts under Environmental Change.”

This project addresses a central challenge in ecology and conservation: how to generate reliable, decision-relevant forecasts of population dynamics in rapidly changing environments. The successful candidate will work at the forefront of near-term ecological forecasting (NTEF), developing and applying ensemble modelling approaches that integrate multiple sources of ecological information to improve predictive performance.

The role offers a unique opportunity to contribute to a highly interdisciplinary programme that combines:

• theoretical and computational modelling,
• experimental validation using high-resolution population data, and
• application to world-leading long-term datasets (e.g. Soay sheep)

The postholder will work closely with an established international team spanning the Universities of Sheffield, Bristol, and Edinburgh, and engage with external partners in the conservation sector. The project places strong emphasis on open science, reproducible workflows, and real-world impact, including the development of forecasting tools for practitioners.

This is an ideal role for a researcher looking to develop independence at the interface of quantitative ecology, statistical modelling, and applied conservation science, while contributing to research with societal relevance.

Applicants must have a PhD (or be close to completion / have equivalent postdoctoral level work experience) in a relevant discipline, such as quantitative ecology, statistics, or a related field along with strong quantitative and analytical skills, with experience applying statistical approaches to ecological or environmental data. Experience with relevant modelling approaches, such as time-series methods or demographic projection models and experience using programming tools for data analysis (e.g. R, Stan or similar), with an emphasis on reproducible workflows are also essential.