The Centre for Doctoral Training in Environmental Intelligence unites cutting-edge data science and AI to address some of the most pressing environmental problems facing human society. Our studentships are diverse in subject, and range from biodiversity conservation and monitoring, to climate resilience and natural hazards.
AI-ecology
Many projects combine computational approaches with on-the-ground data collection through fieldwork. For example, students have used AI models to monitor African savannah species via cameras and acoustic recorders, and developed drone swarms to map rainforests in the Amazon. Other projects have concentrated on marine systems, such as building digital twins of estuaries, fusing in-situ observations with AI to better understand the ocean carbon cycle, and novel approaches to monitoring the biodiversity impacts of off-shore wind farms.
AI-Climate
Climate sciences are another broad theme of work in the CDT. Research includes modelling cloud processes with physics-informed machine learning, predicting transport of Saharan dust, and exploring how agroforestry strategies can achieve net-zero land use.
AI-Environmental ethics
Central to our students’ projects are social and ethical dimensions, with students investigating environmental justice, climate change communication, and how open science can shape equitable research futures. Humans also play an important role, with projects on sustainable urban mobility and active travel development.
Environmental Partnerships
Work in the CDT is highly collaborative, and we work with many organisations, such as WildCRU (Oxford), the National Oceanography Centre, RSK Biocensus, Lion Landscapes, and The Nature Conservancy. Students present their work regularly at international conferences, publish peer-reviewed papers, and co-develop tools with NGOs, government bodies and industry partners.
Together, projects in the CDT represent a commitment to using environmental intelligence to advance academic knowledge, support decision making, and guide on-the-ground practice.