PhD Studentship: Next-generation machine learning for genetic interaction prediction with lab-in the-loop validation in pancreatic cancer

Overview

This PhD studentship focuses on developing next-generation machine learning models for predicting genetic interactions using large-scale perturb-seq data, combined with lab-in-the-loop validation in pancreatic cancer. The project integrates computational modelling with experimental data generated by the AstraZeneca and Cancer Research Horizons Functional Genomics Centre, while the student role remains purely computational.

Who can apply

This opportunity is hosted by the Department of Clinical and Biomedical Sciences at the University of Exeter and funded by the MRC Medical Research Council. The main supervisor specialises in statistical and machine-learning methods for genome editing, with a co-supervisor focusing on statistics, reproducible coding, and model evaluation, alongside additional supervision from Functional Genomics Centre experts.

What you get

The studentship provides funding for tuition fees as well as an annual stipend of £21,805 for the 2026/7 academic year. Students gain expertise in machine learning applied to genomics, single-cell and CRISPR genomics, and high-performance computing, alongside industry-aligned experience through involvement with the Functional Genomics Centre.

Requirements

The position is offered as a full-time, fixed-term contract based on site at the University of Exeter in the United Kingdom. The work involves building prediction models using public perturb-seq datasets, evaluating architectures such as graph neural networks and autoencoders, handling data sparsity and batch effects, and releasing a validated computational framework as open-source software.

Key dates

The closing date for applications is 2026-12-08.