Research Associate x 2 in Deep Learning for Population genomics and biodiversity Forecasting (F[...] in Cambridge

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. The successful candidates will join a large and highly interdisciplinary research group comprising more than 20 PhD students and postdoctoral researchers working across ecology, evolution, conservation, genomics, and artificial intelligence. They will work closely with researchers developing large language model approaches for literature mining, population geneticists generating large genomic datasets, and ecological modellers applying the resulting tools to questions in biodiversity conservation.

The role will involve substantial collaboration both within the University of Cambridge and with national and international partners, providing opportunities to develop innovative AI methodologies while addressing fundamental scientific questions. Candidates should have a PhD in population genetics, evolutionary biology, or a related discipline. A strong quantitative background and substantial experience in deep learning applied to population genetics or evolutionary models are essential. Excellent programming skills in Python and experience with modern machine learning frameworks such as PyTorch or TensorFlow are expected. Experience in ecology, geospatial analysis, or environmental modelling would be advantageous but is not essential.

The successful applicants will be expected to contribute actively to the intellectual life of the group. This includes participating in weekly hackathons and collaborative coding sessions, contributing to the development of shared software infrastructure and open-source tools, mentoring junior researchers where appropriate, and sharing expertise in machine learning and artificial intelligence across the programme. The positions provide an outstanding opportunity to work at the forefront of AI-driven environmental science and to help establish new approaches for understanding biodiversity change in a rapidly changing world.

For more information, please refer to the Further Particulars document. Informal inquiries are welcomed and should be directed to Prof Andrea Manica (am315@cam.ac.uk). Due to the nature of this role, it is based entirely on site. Fixed-term: The funds for this post are available for up to 3 years. Flexible working requests will be considered. The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

£37,694 to £46,049 per annum