Emily Lines

The Lines Research Group at the University of Cambridge is recruiting PhD students to work on forestry topics using remote sensing, data science, and artificial intelligence. The group focuses on highly computational approaches in forest ecology, combining advanced data analysis with field techniques. Applicants should have strong quantitative and computational skills, and a clear interest in ecological research based in forest ecosystems. The group is led by Associate Professor Emily Lines, who encourages prospective students to reach out informally before applying. Funding is available through competitive schemes such as Cambridge Trusts, GATES, and structured PhD programmes, covering stipend, tuition, and research costs. The application deadline for funding is December 2, 2024, and candidates should prepare their CV and research proposal in advance. The group values a collaborative and respectful research culture, and applicants are advised to read the group’s code of conduct and application advice. For more information, visit the group’s website and contact Emily Lines to discuss your fit and ideas.

Emily Lines, Associate Professor at the University of Cambridge and UKRI Future Leaders Fellow, is inviting applications for PhD supervision. The research sits at the intersection of forest ecology , remote sensing , and data science , with project ideas including low-cost terrestrial lidar for forest monitoring , sensing diversity in three dimensions , and three-dimensional foundation models of forests . Applicants are also welcome to develop their own project ideas. This is a PhD opportunity rather than a postdoctoral or master’s opening. The post suggests that funding deadlines for most projects are in early December. No stipend amount, tuition details, or specific funding scheme are stated in the post. Applicants are encouraged to read the supervisor’s advice page before reaching out. Emily Lines also states that she prefers direct, genuine communication from applicants and will not consider proposals or messages written by generative AI. Relevant study areas include environmental science, biology, earth science, and computer science, especially methods involving lidar, forest monitoring, ecological sensing, and computational modelling.