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 at the University of Reading, in collaboration with the Natural History Museum, offers a unique opportunity to investigate the impact of climate and land-use change on the geographical ranges of Madagascar's wild birds. Madagascar is a globally recognized biodiversity hotspot, home to approximately 250 bird species, with 115 found nowhere else. About 15% of these species are threatened with extinction, making conservation efforts in this region particularly urgent. The project leverages both historical museum specimens and modern data, utilizing cutting-edge artificial intelligence and machine learning techniques to quantify changes in species distributions over more than a century. By integrating natural history collections with advanced computational methods, the research aims to uncover how climate change and human activities have reshaped the ranges of Madagascar's birds, and to identify actionable strategies for conservation. Students will work within the EPSRC Centre for Doctoral Training in the Mathematics for our Future Climate, gaining interdisciplinary experience in biodiversity, environmental biology, computer vision, data science, meteorology, and statistics. The project is supervised by a team of experts: Prof K Norris, Dr D Senapathi, Prof E Black, and Dr A.SJ Salili-James. Funding includes a full UKRI stipend and home-level PhD tuition fees. Applicants should have a strong academic background in a relevant discipline (biology, environmental science, computer science, statistics, or related fields), with quantitative and analytical skills. Experience or interest in AI, data science, or machine learning is desirable. International applicants may need to demonstrate English language proficiency. Applications are accepted year-round. To apply, submit your CV and cover letter via the University of Reading application portal, and contact the supervisors for further information if required. For more details, visit the project page: FindAPhD Project Link .