Research Associate (Statistical Population Ecology) in Sheffield

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.

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 (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).

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

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 beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply.

Essential criteria

• 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.
• 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.
• Experience using programming tools for data analysis (e.g. R, Stan or similar), with an emphasis on reproducible workflows.
• Experience contributing to shared code or research databases, including collaborative development practices such as version control.
• Strong problem‑solving skills, particularly in working with uncertain, noisy, or incomplete data.
• Effective written and verbal communication skills, including the ability to present complex ideas clearly.
• Evidence of producing, or clear potential to produce, high‑quality research outputs (e.g. publications, preprints, or 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.

Desirable criteria

• Experience engaging with applied or stakeholder‑relevant research, including translating research outputs for non‑academic users.
• Ability to design, implement, and deliver independent research, contributing to a broader collaborative project.

Salary and Employment Details

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

Benefits

• 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.
• A 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.
• A range of generous family‑friendly policies (paid time off for parenting and caring emergencies, access to menopause support in the workplace, paid time off and support for fertility treatment, and more).

Equality, Diversity & Inclusion

We are a Disability Confident Leader. If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.

Closing date: 16/06/2026.