3542326WR - Research Fellow in Causal Inference and Ecology

University of Southampton; Southampton; On-site; GBP 37,000 - 46,000; Full time

Benefits offered by this job

Flexible working

Family-friendly policies

Disability Confident employer

Job summary

University of Southampton in Southampton invites applications for a Research Fellow in Causal Inference and Ecology. You will develop methods and evaluate causal approaches using ecological data, with partners across UKCEH and NINA.

The role emphasizes methodological development, study design and practical guidance for observational datasets. You will create open-source software and workflows, contributing to best-practice frameworks in ecological studies and monitoring.

Qualifications

• Strong quantitative-modelling skills and experience analysing complex observational data.
• Proficiency in R, Python or related software; reproducible workflows.
• Understanding of causal inference and its application to observational data.

Responsibilities

• Lead methodological development in causal inference and causal prediction for ecology.
• Develop and evaluate workflows using simulated and observational data.
• Create reproducible, open-source software and tutorials for best-practice analyses.
• Collaborate with partners to design ecological studies and monitoring programmes.

Skills

Quantitative modelling

Observational data analysis

Causal inference

Programming (R/Python)

Communication & collaboration

Education

PhD or equivalent in a quantitative field

Tools

Open-science workflows

Job description

3542326WR - Research Fellow in Causal Inference and Ecology

We have an exciting opportunity for a Research Fellow to join the UKRI-funded PRECISE (Precision Ecology for a Changing World) project. You will help establish Precision Ecology, which seeks to predict the effects of conservation interventions in specific ecological contexts. Moving beyond average effects, the project will predict site-specific responses to environmental management and restoration, supporting effective conservation decisions.

Based in the School of Geography and Environmental Science at the University of Southampton, you will work with researchers at Southampton, the UK Centre for Ecology & Hydrology (UKCEH) and the Norwegian Institute for Nature Research (NINA), and international collaborators in statistics, causal inference and ecological modelling.

You will play a leading role in developing and evaluating methods for causal inference and causal prediction in ecology. Using benchmark datasets produced through ecological simulations and large observational datasets assembled by project partners, you will investigate how sampling strategies and data characteristics affect the performance of alternative causal-inference and machine-learning workflows. You will identify which approaches perform best under different conditions and translate the findings into practical guidance.

You will create reproducible, open-source software, analytical workflows, tutorials and guidance. With project partners, you will develop best-practice frameworks for analysing observational datasets and designing ecological studies and monitoring programmes. The role focuses on methodological development, study design and causal prediction, rather than ecological simulation modelling.

You should hold a PhD, or have equivalent professional experience, in a relevant quantitative discipline. We welcome applicants with strong causal-inference and quantitative expertise who want to apply these approaches to ecological questions. Ecological experience is desirable but not essential, provided you are willing to develop relevant ecological and spatial knowledge.

Company: University of Southampton Salary: £37,369 to £45,641 a year Hours: Full-time Location: Southampton, SO17 1BJ Working pattern: On-site Job type: Contract Posting date: 28 Oct 2026 Closing date: 25 Nov 2026

You should have:

• Strong quantitative-modelling skills and experience analysing complex observational data.
• Programming and data-analysis experience using R, Python or related software, including reproducible, well-documented workflows.
• Understanding of causal inference and its application to observational data.
• Understanding of reproducible research and Open Science principles.
• Evidence of independent research and communication through publications, software, reports or equivalent outputs.
• Excellent organisational, communication and collaborative skills.
• Experience in applying causal inference methods, machine learning, geospatial or ecological analysis, or high-performance computing would be advantageous.

The School of Geography and Environmental Science is committed to fostering a culture of equality, diversity and inclusion. We particularly encourage applications from suitably qualified candidates belonging to groups under-represented within University academic posts, including, but not limited to, people from Black and minority ethnic groups and disabled people. We are a signatory to the Concordat to Support the Career Development of Researchers and provide opportunities for career development and mentoring. The School offers family-friendly policies, flexi-time and flexible working. We are a Disability Confident employer and hold a Bronze Athena SWAN award.

For further information, contact Dr Becks Spake at [e-mail].

The expected start date of this role is 1st February 2027.

As a university we aim to create an environment where everyone can thrive and are proactive in fostering a culture of inclusion, respect and equality of opportunity. We believe that we can only truly meet our objectives if we are reflective of society, so we are passionate about creating a working environment in which you are free to bring your whole self to work. With a generous holiday allowance as well as additional university closure days we are committed to supporting our staff and students and open to a flexible working approach.

Proud member of the Disability Confident employer scheme

Disability Confident is a government scheme. It encourages employers to recruit and retain disabled people and those with long term health conditions.