Causal Inference & Ecology Fellow: Site-Specific Predictions

Cyber Security Academy Southampton; Southampton; On-site; GBP 42,000 - 64,000; Full time

Benefits offered by this job

Flexible working

Family-friendly policies

Disability Confident employer

Bronze Athena SWAN award

Job summary

The University of Southampton invites applications for a Research Fellow in Causal Inference and Ecology to help lead methods development for the PRECISE project. You will work with partners across ecologists, statisticians and data scientists to translate causal ideas into practical, site‑specific guidance.

You will produce reproducible analyses and open‑source software, and contribute to tutorials and best‑practice frameworks for study design and observational data use in ecology.

Qualifications

• PhD or equivalent in a quantitative field.
• Strong causal‑inference and quantitative expertise.
• Experience with R, Python and reproducible workflows.
• Ability to produce open‑source software and tutorials.
• Excellent communication and collaboration skills.
• Experience with observational data and causal workflows.

Responsibilities

• Develop and evaluate methods for causal inference in ecology.
• Work with large observational datasets and simulations.
• Create reproducible analyses, software, and tutorials.
• Design studies and provide practical guidance for field projects.

Skills

Quantitative modelling

Causal inference

R/Python programming

Reproducible research

Communication

Education

PhD or equivalent in a quantitative discipline

Tools

R

Python

Job description

The University of Southampton invites applications for a Research Fellow in Causal Inference and Ecology to help lead methods development for the PRECISE project. You will work with partners across ecologists, statisticians and data scientists to translate causal ideas into practical, site‑specific guidance.

You will produce reproducible analyses and open‑source software, and contribute to tutorials and best‑practice frameworks for study design and observational data use in ecology.