University of Cambridge
Research Associate x 2 in Deep Learning for Population genomics and biodiversity Forecasting (Fixed Term)
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Research Associate Positions in Population Genomics and Biodiversity Forecasting
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.
- Responsibilities
- 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.
- Apply the methods to three case studies focusing on African megafauna, European butterflies and moths, and UK pollinators for which we have extensive genomic resources, including time series based on museum specimens.
- Contribute to the development of a new generation of predictive biodiversity models that combine mechanistic understanding with state-of-the-art artificial intelligence.
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Collaboration
- 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.
- 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.
- Collaborate both within the University of Cambridge and with national and international partners, providing opportunities to develop innovative AI methodologies while addressing fundamental scientific questions.
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Qualifications
- 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.
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Contribution to Group
- Contribute actively to the intellectual life of the group, including participating in weekly hackathons and collaborative coding sessions.
- Contribute to the development of shared software infrastructure and open-source tools.
- Mentor junior researchers where appropriate.
- Share expertise in machine learning and artificial intelligence across the programme.
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- Opportunities
- Work at the forefront of AI-driven environmental science and help establish new approaches for understanding biodiversity change in a rapidly changing world.
Application Process
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Informal Inquiries
- Informal inquiries are welcomed and should be directed to Prof Andrea Manica (am315@cam.ac.uk).
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Role Details
- Based entirely on site.
- Interviews are planned for the week commencing Monday, 31 August 2026.
- Fixed-term: The funds for this post are available for up to 3 years.
- Flexible working requests will be considered.
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Apply
- Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
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Enquiries
- For any queries regarding the application process, please contact Zoology HR Office (hr@zoo.cam.ac.uk).
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Reference
- Please quote reference PF50525 on your application and in any correspondence about this vacancy.
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Inclusion
- The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
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Eligibility
- The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
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