PhD Studentship: Can Blood Reveal the Body? Trustworthy AI for Tissue Gene Expression

A blood sample can be collected repeatedly, but a biopsy from the brain, liver or intestine cannot be. Could the molecular signals in the blood reveal what is happening in other tissues, and tell us when the inference is trustworthy?

This PhD will develop Blood2Tissue, a tissue-conditioned AI modelling framework that predicts tissue-specific gene expression programmes from blood. Existing studies demonstrate feasibility for some genes and tissues, but predictions are often population average, accuracy varies substantially, and current approaches rarely identify which individual predictions can be trusted.

You will build on CHROMATIN, our team’s cross-modal attention framework for imputing missing modalities in multi-omics datasets, and MOCHA, our uncertainty framework with a preprint expected shortly. The aim is not simply to create another blood-to-tissue predictor.

You will benchmark existing methods using GTEx dataset, develop and calibrate Blood2Tissue, and use IBDverse and reference cell atlases to investigate fundamental questions such as why some molecular programmes are blood-visible while others remain tissue-autonomous. Confirmed visits to Ellen McDonagh's Open Targets team at European Bioinformatics Institute (EMBL-EBI) will provide experience connecting reliable molecular signals to genetic and disease evidence. The project will deliver reusable computational methodology and new biological understanding of cross-tissue regulation, with longer-term relevance to Digital Twins of living systems.

Training will span transcriptomics, machine learning, uncertainty quantification, genetics, single-cell genomics, HPC, reproducible software and responsible AI, including supervised use of emerging AI-assisted and agentic tools for coding, benchmarking and workflow automation. The project will be conducted at the Earlham Institute, a world-class BBSRC research centre of excellence in genomics, engineering and computational biology, and AI.

You will join Dr Rahuman Sheriff’s AI integrated Digital Twins Group within Earlham Institute’s data-intensive bioscience environment, with Prof Irene Papatheodorou contributing expertise in cellular genomics and FAIR data. You will join a highly collaborative and supportive research community and have access to training and career development opportunities across the Norwich Research Park through the Norwich Research Park Biosciences Doctoral Training Partnership.

Entry requirements

At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5 overall, 6 in each category).

Mode of study

Full-time

Start date

1 October 2027

This project is awarded with a 4-year Norwich Research Park Biosciences Doctoral Training Partnership PhD DTP studentship. The studentship includes payment of tuition fees (directly to the University), a stipend to cover living expenses (2026/27 stipend rate: £21,805, and a Research Training Support Grant of £5,000pa for each year of the studentship.

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