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  Assessment of Brain Tumours Using Machine Learning and MRI Radiomics

   School of Engineering, Computing and Mathematics

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  Dr S Gaudl, Prof E Ifeachor, Prof Oliver Hanemann  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Brain tumours kill more children and adults under the age of 40 than any other cancer but are under-researched compared to most cancers.  A priority is the need for a non-invasive, objective pre-treatment biomarker of brain tumours.  Radiomics, a new and emerging field, combined with Artificial Intelligence (AI), offers the potential not only for the development of such biomarkers, but also for pre-operative prediction of tumour grading and response to treatment.

The aim of the project is to build an AI-based tool for clinicians to use to assess brain tumours to help with treatment planning and to inform clinical decision-making. This will have a significant impact on patient treatment outcome (e.g. a reduction in the number of unnecessary invasive procedures).

The project will use machine learning to correlate radiomic, radiological and histological findings in meningiomas, bench marked against  the gold standard, highly predictive genomic data from meningioma tissue, in order to develop better non-invasive predictive models of tumour grade and clinical outcome.  Such models would help with clinical decisions about surveillance and surveillance intervals vs surgery +/- radiotherapy.

The project will be carried out in collaboration with the Plymouth Centre of Excellence for Brain Tumour which has a biobank of brain tumour samples and associated data, including genomic and MRI data.  This interdisciplinary project brings together clinical expertise in brain tumour and neuro-imaging and expertise in AI, software development, and biomedical data analysis.

The ideal candidate should have a strong background in computer science, data science, engineering or mathematics.


Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification.

If you wish to discuss this project further informally, please contact:

Dr Swen Gaudl, [Email Address Removed];

Professor Emmanuel Ifeachor, [Email Address Removed] or  

Professor Dr C Oliver Hanemann, [Email Address Removed]

For further information on research studentships at the University of Plymouth and to apply for this position please visit: and select the studentship you would like to apply for. Please clearly state the name of the DoS that you are applying for on your personal statement.

Please see here for a list of supporting documents to upload with your application.

For more information on the admissions process generally, please contact [Email Address Removed].

The closing date for applications is 12 noon on 04 May 2022. Shortlisted candidates will be invited for interview shortly after the deadline. We regret that we may not be able to respond to all applications. Applicants who have not received a response within six weeks of the closing date should consider their application has been unsuccessful on this occasion.

Computer Science (8) Engineering (12) Medicine (26)

Funding Notes

The studentship is supported for 3.5 years and includes full Home tuition fees plus a stipend of £16,062.00 per annum (2022/23 rate). The studentship will only fully fund those applicants who are eligible for Home fees with relevant qualifications. Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates (approximately £12,670 per annum).