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.

This PhD project, hosted at the University of Reading, investigates the changing timing of egg laying in UK wild birds in response to climate change. Recent evidence shows that birds are laying eggs earlier, likely due to warmer spring temperatures, but our understanding is limited by a lack of historical data. The project aims to fill this gap by reconstructing long-term phenological time series using both historical and contemporary data. Key objectives include: (1) extracting egg laying timing data from the Natural History Museum’s extensive egg collection using advanced Artificial Intelligence (AI) and Computer Vision (CV) techniques, (2) producing historical baselines covering approximately 150 years, and (3) comparing these with contemporary data from the British Trust for Ornithology to create 200-year time series for up to 200 UK bird species. This will enable estimation of the rate and magnitude of phenological change, assessment of climate change impacts, and exploration of species-specific climate sensitivity and population trends. The project offers comprehensive training in applied AI, biodiversity, and transferable research skills, including a placement with an AI-INTERVENE project partner (3–18 months). Students will have opportunities to present at national and international conferences, enhancing career prospects in interdisciplinary research fields. Applicants should have a BSc and/or MSc in computer science, data science, conservation science, ecology, or related disciplines. Experience with research projects and a strong interest in big data applications to environmental change are desirable. No prior experience with natural history collections is required; full training will be provided. Funding is available for UK students through UKRI, covering Home fees (which increase annually). International students may apply but 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 link. References include key studies on phenological sensitivity to climate across taxa and climate change impacts on natural systems.

This PhD project, hosted at the University of Reading within the AI-INTERVENE department, explores the evolutionary and ecological drivers of flower colour in the Orchidaceae, the largest and most diverse plant family. Flower colour is a key trait mediating interactions with pollinators, dispersers, and herbivores, influencing reproductive isolation and speciation. Despite its ecological and economic importance, the evolution of floral colour across large spatial and evolutionary scales remains poorly understood, with existing data often sparse and subjective. The project aims to fill these gaps by conducting a comprehensive analysis of orchid flower colour evolution, leveraging the remarkable diversity and specificity of orchids to various pollinators. The student will collect high-quality, standardised image data from botanical garden collections, prioritising approximately 2,500 orchid species with available pollinator, spatial, and genetic data. Unlike previous studies, flower images will be characterised in UV-tetrahedral space using artificial intelligence, accounting for differences in visible spectra as perceived by interacting species. Key research questions include: systematic variation of floral colour diversity and visual contrast with latitude and climate; associations between pollination syndromes and regions of floral colour space; evolutionary transitions between pollination systems and corresponding shifts in colour; and the extent to which environmental and ecological variables constrain or promote colour diversification across orchid lineages. The project combines empirical data from living collections with macroevolutionary methods, offering powerful insights into the sensory and ecological drivers of floral colour evolution. Training opportunities include a comprehensive programme in applied AI, biodiversity, and transferable professional and research skills. The student will undertake a placement with an AI-INTERVENE project partner for 3–18 months and present research at national and international conferences, positioning them at the forefront of the discipline and enhancing future employment prospects. Applicants should have a degree in biology, plant science, ecology, or a closely related environmental or physical science, or be computer science students interested in evolutionary biology. Experience with data handling and analysis (e.g., R) is desirable but not essential, as training will be provided. Enthusiasm and a strong work ethic are required. Funding is subject to competition, with UKRI funding covering Home fees only. International students may apply but must cover the difference between International and Home fees. The application deadline is January 19, 2026. For more information and to apply, visit the project link.