Department of Zoology
Two Research Associate posts are available in the Department of Zoology at the University of Cambridge to develop advanced deep learning approaches for population genomics and 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 these posts is the development of novel deep learning methods that enhance the inference, scalability, and predictive performance of the CISGeM framework.
The successful candidates will design and implement deep learning models capable of integrating heterogeneous data sources, including genomic variation, species occurrence records, climate reconstructions, environmental layers, and remotely sensed observations. The methods will be applied to three case studies focussing on African megafauna, European butterflies and moths, and UK pollinators for which we have extensive genomic resources, including time series based on museum specimens. The researchers will contribute directly to the development of a new generation of predictive biodiversity models that combine mechanistic understanding with state-of-the-art artificial intelligence.