Enable job alerts via email!

Senior Research Associate / Research Associate in Experimental Ecology

University of Bristol Law School

Bristol

Hybrid

GBP 39,000 - 51,000

Full time

Today
Be an early applicant

Job summary

A leading research institution in Bristol is seeking a Postdoctoral Researcher to lead the STABILI-NICHE project focusing on ecological modelling for biodiversity conservation. Candidates should have a PhD in quantitative ecology, strong skills in statistical methods including Bayesian approaches, and proficiency in R. The role offers hybrid working options and a competitive salary of £39,906 - £50,253 per annum depending on experience.

Benefits

Funding for conference attendance
Opportunities for collaboration and postgraduate supervision

Qualifications

  • PhD (or near completion) in quantitative ecology, conservation biology, or related field required.
  • Skilled in statistical modelling, particularly Bayesian and spatio-temporal methods.
  • Experience with niche modelling, multivariate analyses, and GIS.

Responsibilities

  • Lead quantitative analysis and modelling for STABILI-NICHE.
  • Integrate large-scale population datasets with climate and land-use data.
  • Develop tools for assessing stability in data-deficient species.

Skills

Statistical modelling
Bayesian methods
Spatio-temporal methods
Niche modelling
GIS
Multivariate analyses
Communication of quantitative findings

Education

PhD in quantitative ecology, conservation biology, or related field

Tools

R
Job description

The role

A postdoctoral position is available on patterns of resilience loss across space and through time, under the guidance of Assoc. Prof. Christopher Clements (University of Bristol) in collaboration with Prof. Dylan Childs and Prof. Andrew Beckerman (University of Sheffield). This is a full-time, 3-year post starting 1 January 2026.

The STABILI-NICHE project will use niche theory and a global dataset of >1.8M time series from 6,700 chordate species to build n-dimensional abiotic niches and estimate each population`s position within its species` niche space. We will assess how niche marginalisation - movement towards niche edges - affects population stability, model the impacts of changes in key niche components (e.g. temperature, rainfall) individually and in combination, and project how stability will shift spatially and temporally over coming decades. This will identify at-risk species and regions, develop tools for assessing stability in data-deficient species, and provide next-generation modelling approaches to inform conservation priorities.

The project has a strong conservation focus, involving close engagement with local partners (e.g. West of England Nature Partnership) and international NGOs (e.g. Zoological Society of London). Funding is available for conference attendance and collaborator visits. The successful candidate will join a diverse research group working on ecological dynamics via experimental, modelling, and large-scale data approaches, with opportunities for collaboration and postgraduate supervision.

Hybrid working is available - flexibility of working up to 3 days per week from home.

What will you be doing?

You will lead the quantitative analysis and modelling for STABILI-NICHE, integrating large-scale population datasets with high-resolution climate and land-use data, constructing multi-dimensional niche models, and applying advanced Bayesian spatio-temporal methods. You will:

  • Build n-dimensional abiotic niches for >6,700 species and estimate population positions within them.
  • Quantify niche marginalisation and assess its effects on population stability.
  • Model how individual and combined niche component changes influence stability loss rates.
  • Project stability change across space and time to pinpoint emerging risk areas.
  • Develop tools for assessing stability in data-deficient species and strategies for conservation planning.
  • Work closely with conservation stakeholders to ensure outputs inform biodiversity management.
You should apply if
  • You have a PhD (or near completion) in quantitative ecology, conservation biology, or a related field.
  • You are skilled in statistical modelling, particularly Bayesian and spatio-temporal methods (e.g. R-INLA, GMRFs), and proficient in R.
  • You have experience with niche modelling, multivariate analyses (e.g. PCA), GIS, and integrating climate/land-use data with biological datasets.
  • You understand concepts in ecological stability, resilience, and biodiversity change.
  • You can communicate complex quantitative findings clearly to academic and practitioner audiences.
  • You enjoy collaborative, interdisciplinary research and are committed to open, reproducible science.

The role offers flexibility to align analyses with your expertise, opportunities to supervise students, and scope to contribute to conservation policy via NGO and governmental engagement.

Additional information

For informal enquiries please contact Professor Chris Clements - c.clements@bristol.ac.uk

Contract type: Open ended (with fixed funding until 31/12/2028)

Work pattern: Full-time

Grade: I or J

Salary: £39,906 - £44,746 (Grade I) / £43,482 - £50,253 (Grade J) per annum depending on experience

School/Unit: Biological Sciences

This advert will close at 23:59 UK time on Sunday 26th October 2025

Interviews will be held on Tuesday 4th November 2025

Our strategy and mission

We recently launched our strategy to 2030 tying together our mission, vision and values.

The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives - particularly people of colour, LGBT+ and disabled people - because diversity of people and ideas remains integral to our excellence as a global civic institution.

Get your free, confidential resume review.
or drag and drop a PDF, DOC, DOCX, ODT, or PAGES file up to 5MB.