Applications are invited for a three-year Postgraduate studentship, supported by Help Harry Help Others (HHHO) and the College of Health and Life Sciences, to be undertaken within the Artificial Intelligence in Paediatric Neuroimaging (AIPNI) Research Group at Aston University. The successful applicant will join an established experimental group working on machine learning methods for paediatric neuroimaging.
The position is available to start in October 2022Background to the Project
Despite the prognostic benefits of early diagnosis, histopathological analysis performed post-resection is the current gold standard for the differential diagnosis of paediatric brain tumours. Advances in non-invasive, pre-surgical diagnostic methods are thus required to inform tumour resection, consequently improving patient outcomes. The proposed research will utilise advanced magnetic resonance imaging (MRI) techniques, including magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI), to develop a diagnostic classification tool that improves pre-surgical posterior fossa tumour diagnosis in children. Machine learning algorithms will be used to determine which MRI technique, or combination of multi-modal techniques, has the highest diagnostic accuracy to produce the optimal differential diagnostic classifier. Subsequent employment of data harmonisation procedures will further optimise the machine learning classifier for heterogeneous, incomplete data that simulates clinical “realities”, thus determining the potential for future clinical implementation. Neuroradiologist collaborators will then provide recommended diagnoses following the examination of two individual groups of paediatric brain tumour MRI datasets, of which only one will incorporate the diagnostic classifier outputs. The accuracy of diagnosis will be compared between the datasets to identify any changes in diagnostic certainty attributable to inclusion of the classifier outputs. The end-goal of this project is to therefore establish a method of improving pre-surgical paediatric brain tumour diagnosis through the development of a prototype machine learning diagnostic classifier that provides accurate, early diagnostic information. This classifier will have the potential for substantial clinical impact, informing the surgical management of paediatric brain tumour cases and thus improving patient outcomes.
Person Specification
The successful applicant should have been awarded, or expect to achieve, a degree in a relevant subject with a 60% or higher weighted average, and/or a First or Upper Second Class Honours degree (or an equivalent qualification from an overseas institution) in Psychology, Physics, Chemistry, Neuroscience, Computer Science or any relevant scientific subject. Preferred skill requirements include knowledge/experience of Nuclear Magnetic Resonance (NMR or MRI), programming, image analysis and data manipulation.
Contact information
For formal enquiries about this project contact Dr Jan Novak by email at [Email Address Removed].
Submitting an application
As part of the application, you will need to supply:
· A copy of your current CV
· Copies of your academic qualifications for your Bachelor degree, and Masters degree (if studied); this should include both certificates and transcripts, and must be translated in to English
· Two academic references
· Proof of your English Language proficiency
· A research proposal statement:
o The application must be accompanied by a “research proposal” statement. An original proposal is not required as the initial scope of the project has been defined, candidates should take this opportunity to detail how their knowledge and experience will benefit the project and should also be accompanied by a brief review of relevant research literature. Please include the supervisor’s name, project title and project reference in your Personal Statement.
Details of how to submit your application, and the necessary supporting documents can be found here.
Please select “Research Neurosciences” from the application form options.
If you require further information about the application process please contact the Postgraduate Admissions team at [Email Address Removed]
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