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.

Essential requirements:
• 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.

Desirable experience includes: applying causal inference methods, machine learning, geospatial or ecological analysis, or high-performance computing.

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 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.

In your application please include a covering letter that uses clear examples to show how your knowledge, experience and skills meet the essential criteria in the person specification.