Department of Zoology
A Research Associate post is available in the Department of Zoology at the University of Cambridge to develop advanced large language model (LLM) approaches for biodiversity forecasting as part of a major research programme investigating how species and ecosystems respond to environmental change. The project aims to transform biodiversity prediction by integrating ecological, genomic, climatic, and environmental data within a unified modelling framework known as Climate-Informed Spatial Genomic Models (CISGeMs). These models provide a powerful mechanism for reconstructing population histories and forecasting future biodiversity trajectories, creating new opportunities to understand and predict biological responses to climate change at unprecedented spatial and temporal scales.
The principal aim of this post is the development of agentic LLM-based systems that can extract, organise, and validate biodiversity information from the published scientific literature at unprecedented scale. The successful candidate will design and implement AI workflows capable of processing more than one million scientific papers to identify and extract georeferenced information on species distributions, ecological interactions, demographic processes, environmental associations, and other biodiversity-relevant data. These data will form a key component of the CISGeM framework, complementing genomic, climatic, and environmental datasets and enabling a richer representation of biodiversity dynamics through space and time. The researcher will contribute directly to the development of a new generation of biodiversity forecasting models that combine mechanistic understanding with state-of-the-art artificial intelligence.