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‘Artificial Intelligence to Enable Multi-Omics Integration - Delivering Enhanced Data Utilisation for Biomarker Discovery’ (Ref FHMS - FF - 01 BIO)

   Faculty of Health & Medical Sciences

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  Prof Nophar Geifman  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

This is a great opportunity for an enthusiastic student to be trained in the cutting-edge disciplines of Artificial Intelligence (AI) and Machine Learning (ML), intersected with medicine and biology, as well as biomarker technologies for characterisation of human disease and wellness. The project also offers real-world data analysis skills development through the use of the world-leading UK Biobank. Such a training offers great advantage for a career in research or industry. Specifically, in this studentship we focus on the increasing need in medicine for molecular indicators (biomarkers) in a range of diseases. These can be identified in blood or other body fluids and enable more precise patient diagnosis as well as prediction of disease progression and response to treatments. New biomarkers can help make better predictions for patients, improving how they are cared for and improving the health economy. We have reached a point where we can generate huge amounts of data on the types of molecules found in blood or urine, using advanced ‘omics techniques that measure hundreds or thousands of different molecules such as proteins, lipids, or metabolites. While individual analyses that use genomic or other ‘omic data have shown some predictive abilities, there is growing evidence that multi-‘omic models, where biomarkers are identified across multiple ‘omic platforms, can enhance predictions for patients. However, combining these very complex data is not straightforward. In this PhD, we will directly address that issue. AI gives us the ability to combine complex data from different sources, assessing molecular signatures that give us the ability to make diagnoses or determine who will respond well or badly to a drug. Using the huge amounts of data already available to us, we will develop and assess novel machine-learning approaches that can improve biomarker discovery through making better use of all the data collected from patients.

A Master’s degree in a relevant subject/discipline with some prior experience in AI/machine learning is desirable. The successful candidate will work with a multidisciplinary team of researchers based at the University of Surrey, and nationally. The project is expected to yield a number of high-impact publications and support will be provided for developing skills in research design, data analysis, and peer reviewed publication.

Principle Supervisor - Nophar Geifman

Prof Geifman has expertise in applied health and biomedical informatics. Her interests lie in data sciences within healthcare and medicine; extending the use of artificial intelligence and big-data analytics to improve patient-centric predictions, treatment and outcomes. Her research centres on precision medicine, patient stratification, and biomarker discovery; she currently leads on the use of machine learning for endotype and biomarker discovery on a number of national projects. She has significant experience in developing and deploying novel methodological approaches to the analysis of large, diverse health data, and the integrative analysis of multilevel clinical and biological information.

[Email Address Removed]

Entry requirements

Open to UK and international students with the project starting in October 2023. Note that a maximum of 30% of the studentships will be offered to international students.

You will need to meet the minimum entry requirements for our PhD programme https://www.surrey.ac.uk/postgraduate/biosciences-and-medicine-phd#entry.

How to apply

Applicants are strongly encouraged to contact the relevant principal supervisor(s) to discuss the project(s) before submitting their application.

Applications should be submitted via the [https://www.surrey.ac.uk/postgraduate/biosciences-and-medicine-phd programme page (N.B. Please select the October 2023 start date when applying).

You may opt to apply for a single project or for 2 of these Faculty-funded studentship projects.

When completing your application, in place of a research proposal, please provide a brief motivational document (1 page maximum) which specifies:

  • the reference numbers(s) for the project or two projects you are applying for
  • the project title(s) and principal supervisor name(s)
  • if applying for two projects, please also indicate your order of preference for the projects
  • an explanation of your motivations for wanting to study for a PhD
  • an explanation of your reasons for selecting the project(s) you have chosen

Additionally, to complete a full application, you MUST also email a copy of your CV and 1-page motivational document directly to the relevant project principal supervisor of each project you apply for. Due to short turnaround times for applicant shortlisting, failure to do this may mean that your application is not considered.

Please note that online interviews for shortlisted applicants are expected to take place during the week commencing 30th January.

Funding Notes

Funding is for 3.5 years and includes UKRI-aligned stipend (£17,668 pa for 2022-23), approved University of Surrey fees and a research budget. This studentship is funded by Faculty of Health and Medical Sciences, University of Surrey.
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