Data-driven AI approaches to improving reproducibility and discovery in ‘omics biomarker research

Data-driven AI approaches to improving reproducibility and discovery in ‘omics biomarker research - in collaboration with the SEISMIC facility at the University of Surrey.

Start date

1 October 2024


3.5 years

Application deadline

Funding information

UKRI standard stipend - £18,622 for 2024-25.

Tuition fee covered, research training supports up to £1,000 per year. Only candidates who pay UK/home rate fees are eligible for the studentship/scholarship. International students are welcome to apply but they will need to either self-fund or secure funding from other sources.


The successful candidate will join the Digital Health Research Cluster within the School of Health Sciences in working on data-driven approaches to improving reproducibility in metabolomics and proteomics, the quantitative study of the molecules that are the building blocks of life. Reproducibility in ‘omics work is crucial to allow for validation of biomarkers and pathways associated with different conditions, in a field that generates vast amounts of data. Data standards and principles exist to address this problem, such as the FAIR principles for scientific data management and stewardship. These state that data should be Findable, Accessible, Interoperable, and Reusable. 

The aim of this Ph.D. programme of work is to improve findability, accessibility and reusability of data in ‘omics work through data-driven approaches, and in collaboration with the SEISMIC facility at the University of Surrey. The project is organised as a series of three studies, each of which will lead to a research publication.

  • Study 1: Participate in a Systematic Evidence Map of data availability in metabolomics. The student will receive training on survey methodology, software development and metabolomics research in this year.
  • Study 2: Develop a data-driven approach to the identification of metabolites and lipids in direct infusion mass spectrometry experiments, with the goal of developing this as an open-source Python library. This has the potential to replace current approaches which rely upon custom workflows and internal libraries, which by definition are not FAIR means of metabolite annotation. Although primarily working with data outputs, the student will also receive training in the operation of mass spectrometry instruments in the SEISMIC facility.
  • Study 3: Develop a machine learning approach to the validation of metabolite annotations in liquid-chromatography mass-spectrometry through the identification of latent variables governing retention time, using data from publicly available databases such as ChemSpider.

Eligibility criteria

You will need to meet the minimum entry requirements for the Health Sciences PhD programme.

Open to UK nationals and those who pay UK/home rate fees. See UKCISA for further information

How to apply

In the first instance, applicants should contact the supervisor at for an introductory discussion. Applications should be submitted via the Health Sciences PHD programme page. In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.

Health Sciences PHD

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Application deadline

Contact details

Matt Spick

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