Research Associate; Statistical Population Ecology

Research Associate (Statistical Population Ecology) Overview

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." The 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
• 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.

The Research Associate will contribute to all aspects of the project, with a primary focus on the development and evaluation of forecasting models.

• Develop, implement, and evaluate statistical and computational models for near‑term population forecasting, including
  • time‑series (e.g. state‑space / MARSS) and
  • demographic (e.g. IPM / MPM) approaches.
• Design and test ensemble modelling frameworks, including hierarchical/meta‑model approaches for combining forecasts.
• Conduct simulation studies to evaluate forecasting performance across ecological and data scenarios.
• Analyse complex ecological datasets, including experimental microcosm data and long‑term field datasets.
• Contribute to the development of robust, reproducible analytical pipelines in R (or similar environments).
• Integration across work packages—work across simulation, experimental, and real‑world applications to assess model performance under different sources of uncertainty.
• Contribute to the application of forecasting approaches to long‑term population datasets (e.g. Soay sheep).

• Publish research findings in high‑quality peer‑reviewed journals.
• Present results at national and international conferences and project meetings.
• Contribute to the development of open‑source tools, codebases, and documentation to support uptake of forecasting methods.

• Work collaboratively with project partners across institutions and disciplines.
• Contribute to project meetings, workshops, and synthesis activities.
• Engage with non‑academic stakeholders (e.g. conservation organisations) to support the development of tools and outputs.

• Support the supervision of postgraduate research students where appropriate.
• Maintain high standards of data management, documentation, and research integrity.
• Carry out other duties, commensurate with the grade and remit of the post.

We welcome applications from all candidates with strong quantitative expertise relevant to this role. We are particularly interested in applicants from ecological, statistical, mathematical, or closely related disciplines who can bring rigorous analytical approaches to population forecasting under environmental change. We recognise that excellent candidates may have developed these skills in different research contexts, and we value diverse disciplinary pathways where they demonstrate the technical competencies required for the post.

Our diverse community of staff and students recognises the unique abilities, backgrounds, and…