University of Cambridge
Research Associate in Large Language Models for Biodiversity Data Extraction (Fixed Term)
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Research Associate in Advanced Large Language Models for Biodiversity Forecasting
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.
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Key Responsibilities
- Design and implement AI workflows capable of processing more than one million scientific papers.
- Identify and extract georeferenced information on species distributions, ecological interactions, demographic processes, environmental associations, and other biodiversity-relevant data.
- Contribute to the development of Climate-Informed Spatial Genomic Models (CISGeMs).
- Work closely with researchers developing deep learning approaches for population genomic inference, population geneticists generating large genomic datasets, and ecological modellers applying the resulting tools to questions in biodiversity conservation.
- Collaborate with other projects across the University of Cambridge focused on large-scale knowledge extraction and retrieval from the scientific literature.
Qualifications and Skills
- PhD in computer science, machine learning, artificial intelligence, bioinformatics, computational biology, or a related discipline.
- Strong quantitative background and substantial experience working with large language models, natural language processing, information extraction, retrieval-augmented generation, or agentic AI systems.
- Excellent programming skills and experience with modern machine learning ecosystems and tools.
- Experience in ecology, biodiversity science, geospatial analysis, or scientific text mining would be advantageous but is not essential.
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Additional Information
- The successful applicant will be expected to contribute actively to the intellectual life of the group.
- 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. Due to the nature of this role, it is based entirely on site.
How to Apply
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
Contact Information
Informal enquiries are welcomed and should be directed to: Prof Andrea Manica email: am315@cam.ac.uk
If you have any queries regarding the application process, please contact Zoology HR Office email: hr@zoo.cam.ac.uk
Please quote reference PF50528 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
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