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University of Exeter

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

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Dec 8, 2026

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Country

United Kingdom

University

University of Exeter

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Keywords

Computer Science
Biomedical Engineering
Cancer Biology
Biology
Single-cell Analysis
Medical Science
Pancreatic Cancer
Genomic
Statistics
Bioinformatic
ML

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About this position

[Fees plus stipend of £21,805 per annum for 2026/7.]

PhD studentship at the University of Exeter on next-generation machine learning for predicting genetic interactions in cancer, with a specific focus on pancreatic cancer. The project sits at the interface of computational genomics, machine learning, and cancer biology, and is funded by the Medical Research Council (MRC).

The research tackles a major challenge in oncology: identifying genetic interactions, including synthetic lethality, that only appear when two genes are disrupted together. Because exhaustive experimental testing is infeasible, the project will develop computational models that learn from large-scale perturb-seq data, combining CRISPR perturbations with single-cell transcriptomics. Initial model development will use public perturb-seq datasets, while later stages will be supported by experimental data generated through the AstraZeneca and Cancer Research Horizons Functional Genomics Centre in pancreatic cancer models.

You will explore modern machine learning approaches such as graph neural networks, autoencoders, and related architectures for learning joint gene embeddings from sparse, high-dimensional transcriptomic profiles. The project also addresses scalability to genome-wide prediction, robustness to batch effects, and generalisation across cancer contexts. The work is computational, but it is embedded in a lab-in-the-loop framework: experimental results will be used to refine and validate the model in pancreatic cancer.

The University of Exeter will provide high-performance computing resources for model development and training, and the Functional Genomics Centre will contribute CRISPR and single-cell screening expertise and data. The expected outcomes include open-source software for genetic interaction prediction, experimentally validated interactions in a pancreatic cancer cell line, and a reproducible workflow that integrates computation with experiment while controlling bias and false discovery rates.

Funding is provided for fees plus a stipend of £21,805 per annum for 2026/7. The application deadline is 2026-12-08. This opportunity is well suited to candidates with interests in machine learning, genomics, statistics, reproducible analysis, and cancer-focused computational biology.

Funding details

Available

What's required

Applicants should have a strong interest in computational genomics, machine learning, and cancer research. The project is described as purely computational, so experience with programming, reproducible coding, model evaluation, statistics, single-cell data analysis, CRISPR genomics, or high-performance computing would be advantageous. No specific degree class, language test, or minimum GPA is stated in the posting.

How to apply

Apply through the University of Exeter funding application link. Review the full studentship details before submitting your application. Ensure your materials are submitted by 2026-12-08.

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