LLM Scientist for Biodiversity Data Extraction

University of Cambridge; Cambridge; On-site; GBP 45,000 - 60,000; Full time

Job summary

University of Cambridge is seeking a Research Associate in the Department of Zoology to develop advanced LLM approaches for biodiversity forecasting within the CISGeMs framework. The role involves creating AI workflows to process millions of scientific papers, extract georeferenced species data, and integrate ecological, genomic, and climatic information for predictive modelling.

Collaboration spans ecology, genomics, and AI research groups.

Qualifications

• PhD in computer science, machine learning, artificial intelligence, bioinformatics, computational biology, or related discipline.
• Strong quantitative background with experience in LLMs, NLP, information extraction, retrieval-augmented generation or agentic AI systems.
• Excellent programming skills and experience with modern ML ecosystems and tools.

Responsibilities

• Develop agentic LLM-based systems to extract, organize and validate biodiversity information from literature.
• Design and implement AI workflows to process over one million papers for georeferenced biodiversity data.
• Collaborate with researchers across ecology, genomics and environmental data domains.
• Mentor junior researchers and contribute to shared software infrastructure and open-source tools.

Skills

AI workflow design

NLP / Information extraction

Programming in ML stacks

Interdisciplinary collaboration

Education

PhD in CS/ML/AI/bioinformatics

Tools

Python

PyTorch/TensorFlow

Job description

University of Cambridge is seeking a Research Associate in the Department of Zoology to develop advanced LLM approaches for biodiversity forecasting within the CISGeMs framework. The role involves creating AI workflows to process millions of scientific papers, extract georeferenced species data, and integrate ecological, genomic, and climatic information for predictive modelling.

Collaboration spans ecology, genomics, and AI research groups.