Deep Learning Research Associate - Biodiversity Genomics

University of Cambridge; Cambridge; On-site; GBP 42,000 - 59,000; Full time

Job summary

University of Cambridge invites two Research Associate posts in the Department of Zoology to develop advanced deep learning approaches for population genomics and biodiversity forecasting within CISGeMs.

Successful candidates will design and implement deep learning models that integrate genomic variation, species occurrences, climate and environmental data, contributing to a major programme on biodiversity dynamics and AI-driven environmental science.

Qualifications

• PhD in population genetics, evolutionary biology, or related discipline.
• Strong quantitative background and deep learning experience in population genetics or evolutionary models.
• Excellent Python programming and experience with PyTorch or TensorFlow.

Responsibilities

• Design and implement deep learning models for CISGeMs.
• Integrate heterogeneous data sources including genomic variation, occurrence records, climate and environmental layers.
• Contribute to development of predictive biodiversity models that combine mechanistic understanding with AI.
• Participate in weekly hackathons and collaborative coding sessions; contribute to shared software infrastructure.
• Mentor junior researchers and share expertise in machine learning across the programme.

Skills

Deep learning

Python

Data integration

Education

PhD in population genetics

PhD in evolutionary biology

Tools

PyTorch

TensorFlow

Job description

University of Cambridge invites two Research Associate posts in the Department of Zoology to develop advanced deep learning approaches for population genomics and biodiversity forecasting within CISGeMs.

Successful candidates will design and implement deep learning models that integrate genomic variation, species occurrences, climate and environmental data, contributing to a major programme on biodiversity dynamics and AI-driven environmental science.