Research Associate in Biodiversity and Pollution Modelling

Job ref: BUS00863

Salary: £49,017 - £57,582 per annum

Closing date: 02 July

About the role

Can Environmental Policy Protect Biodiversity? Help Us Find Out.

Do you want your research to shape how we understand the ecological consequences of climate policy? Are you driven by data, causal rigour, and real-world impact?

The Centre for Health Economics & Policy Innovation (CHEPI) at Imperial Business School is recruiting a Research Associate to join an exciting interdisciplinary project at the frontier of environmental economics and biodiversity science.

Governments around the world are regulating greenhouse gas emissions — but the wider ecological consequences of these policies remain poorly understood. This Royal Society-funded project will deliver the first causal and monetised estimates of biodiversity impacts arising from the EU Emissions Trading System (EU ETS), embedding findings within a planetary health framework that connects climate policy, air quality, and ecological change.

You will be supervised by Dr Laure de Preux (Imperial Business School) and Dr Will Pearse (Department of Life Sciences), working across disciplinary boundaries to produce research that is both scientifically rigorous and directly relevant to policymakers.

What you would be doing

• Integrate and harmonise large-scale biodiversity, environmental, and industrial emissions datasets across Europe
• Apply causal inference methods to identify the impact of EU ETS regulation on local co-pollutant emissions
• Develop species distribution models and analyse biodiversity responses to policy-induced environmental change
• Contribute to the economic valuation of biodiversity outcomes within a planetary health cost-benefit framework
• Produce academic publications, policy briefs, and reproducible research outputs for scientific and policy audiences

What we are looking for

We are seeking a quantitative researcher with:

• A PhD (or equivalent) in biodiversity science, environmental science, geospatial modelling, data science, or a related quantitative field.**Candidates with a PhD near to completion will be considered for appointment as a Research Assistant, with promotion on successful completion** (after all corrections have been accepted by the examiners and the awarding university has accepted this).**
• Strong experience with large, complex spatial or environmental datasets
• Proficiency in R or Python and strong quantitative modelling skills
• Ability to analyse complex data and produce clear, policy-relevant insights.
• Excellent written and verbal communication skills, with the ability to disseminate research findings to diverse audiences

Experience with species distribution modelling, causal inference methods, or environmental exposure modelling is particularly welcome.

What we can offer you

• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
• As a member of research staff you have 10 development days to use to develop your skills and explore your career prospects
• Sector-leading salary and remuneration package (including 41 days off a year and generous pension schemes).
• Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further information

This post is Full-Time and Fixed Term initially for 22 months, with an anticipated start date of around October 2026. Some remote working may be possible, but the postholder should expect to spend at least three days per week on campus and to participate actively in seminars, research meetings, and the wider academic life of the relevant departments.

Interviews will be conducted online in early July 2026.

For informal enquiries, please contact Dr Laure de Preux (LdePreux@imperial.ac.uk) or Dr Will Pearse (Will.Pearse@imperial.ac.uk).