Research Associate (Statistical Population Ecology) in Sheffield

Seeking a highly motivated, quantitatively skilled Research Associate for the NERC-funded project “Harnessing Ensemble Models for Robust Near‑Term Population Forecasts under Environmental Change.” The project tackles the challenge of generating reliable, decision‑relevant forecasts of population dynamics in rapidly changing environments. The successful candidate will develop and apply 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 seeking to develop independence at the interface of quantitative ecology, statistical modelling, and applied conservation science while contributing to research with societal relevance.

Main duties and responsibilities

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 approaches (e.g. state‑space, MARSS)
• demographic approaches (e.g. IPM, MPM)
• 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).
• Integrate 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).

Dissemination and outputs

• 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.

Collaboration and project contribution

• 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.

Wider contributions

• 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.

Person Specification

Applicants should possess a PhD (or be close to completion) in a quantitative discipline such as quantitative ecology, statistics, or a related field, and demonstrate:

• Strong quantitative and analytical skills and experience applying statistical approaches to ecological or environmental data.
• Experience with relevant modelling approaches, including time‑series methods or demographic projection models.
• Experience using programming tools for data analysis (e.g. R, Stan) with a focus on reproducible workflows.
• Experience contributing to shared code or research databases, including collaborative development practices such as version control.
• Experience engaging with applied or stakeholder‑relevant research, translating research outputs for non‑academic users.
• Ability to design, implement, and deliver independent research, contributing to a broader collaborative project.
• Strong problem‑solving skills, particularly in dealing with uncertain, noisy, or incomplete data.
• Effective written and verbal communication skills, including the ability to present complex ideas clearly.
• Evidence of producing high‑quality research outputs (publications, preprints, reports).
• Good organisational and time‑management skills, with the ability to manage multiple priorities and meet deadlines.
• Commitment to high standards of research practice, including data management, documentation, and reproducibility.

Benefits

• A minimum of 41 days annual leave including bank holiday and closure days (pro‑rata) with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• Variety of staff networks, providing opportunities for social interaction, peer support and personal development (e.g. Race Equality, LGBT+, Women’s and Parent’s networks).
• Recognition awards to reward staff who go above and beyond in their role.
• Family‑friendly policies, including paid time off for parenting and caring emergencies, access to menopause support in the workplace, paid time off and support for fertility treatment.

Working conditions

• Grade: 7
• Salary: £38,784 – £39,906 per annum
• Work arrangement: Full‑time (100% FTE)
• Duration: Fixed‑term, available from 1 July 2026 for a period of 36 months
• Line manager: Professor of Population Ecology
• Direct reports: None
• Contact: Professor Dylan Childs

Equal Opportunity Statement

We are a Disability Confident Leader. Candidates with a disability who meet the essential criteria will be invited to the next stage of the selection process.