This PhD project, hosted at the University of Reading within the AI-INTERVENE department, explores the evolutionary dynamics of the cat family Felidae, including both living and fossil species. Felids are a globally distributed group of predatory mammals, with a rich evolutionary history spanning approximately 30 million years. The research aims to resolve longstanding questions about the diversification, dispersal, and adaptive evolution of both modern conical-toothed cats and extinct sabre-toothed cats, using advanced computational and statistical approaches.

The project will employ machine learning classification to identify morphological features that distinguish felid taxa and inform phylogenetic relationships. These features will underpin Bayesian phylogenetic inference and divergence dating, enabling the construction of dated phylogenetic trees to test the timing and rate of felid diversification. By integrating geographical distribution data and reconstructed palaeoclimate models, the research will investigate whether speciation events coincided with geographic dispersal and how environmental changes influenced felid biodiversity through time.

This interdisciplinary project combines evolutionary biology, ecology, data science, and statistics, offering a unique opportunity to apply cutting-edge AI and computational methods to address questions relevant to the current biodiversity crisis. The student will receive comprehensive training in applied AI, biodiversity research, and transferable professional skills. A placement with an AI-INTERVENE project partner (3–18 months) is included, and the student will have opportunities to present at national and international conferences, enhancing future career prospects.

Eligibility: Applicants should have a degree in zoology, environmental or physical science, or data science. Experience with R and an interest in museum specimens are beneficial but not required. Funding is available for UK students through UKRI, covering Home fees only; international applicants must cover the difference between International and Home fees. Funding is awarded competitively to the strongest applicants. The application deadline is January 19, 2026.

For more information and to apply, visit the project page or contact the department. This is an excellent opportunity for students interested in evolutionary biology, machine learning, and biodiversity research to contribute to a high-impact project at a leading UK institution.