|UK Students, EU Students, International Students
|8th December 2023
|12th January 2024
The AIM (Advanced Inter-Disciplinary Models) DTP is funded by the MRC between three Partners – the Universities of Birmingham, Leicester and Nottingham – and three more Associate Partners – the Research Complex at Harwell, Mary Lyon Centre and Rosalind Franklin Institute. We have a range of exciting and diverse PhD 4-year projects at all 3 partner Institutions which are now open for a September 2024 start and those available at The University of Nottingham are detailed below.
Projects with an industry partner (iCASE projects) offer a unique opportunity to undertake translational research and come with a mandatory placement requirement and an enhanced stipend.
Full information about funding of these projects and application details, including application form plus Equality, diversity and inclusion form are available at https://more.bham.ac.uk/mrc-aim/phd-opportunities/.
The deadline for submitting applications is 12.00 am GMT, Friday, 12 January 2024. Interviews will take place during the week commencing 26 February and will be held via Zoom.
Applicants must hold, or be about to obtain, a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant subject. A master’s qualification in a related area could be beneficial, as could additional relevant research experience.
Full details can be found on the MRC website.
School of Psychology
Project Title: Optimising patient selection for Deep Brain Stimulation in Parkinson's disease using multimodal machine learning (iCASE)
Supervisors: Mark Humphries, firstname.lastname@example.org ,Dr JeYoung Yung (UoN)
Industrial Supervisors: Dr Jonathan O’Keefe (Machine Medicine Technologies Ltd (London) and St George’s Hospital, London. Parkinson’s clinical team led by Dr Francesca Morgante
Parkinson’s disease has debilitating motor symptoms of tremor in the limbs, slowness of movement, and freezing, unable to move. A highly effective treatment is electrical stimulation deep in the motor regions of the midbrain. But surgery for this deep brain stimulation is only offered to around 2% of all patients, and about a quarter of those who receive it have poor outcomes. Optimising the selection of patients for deep brain stimulation will widen access to treatment, improve treatment outcomes, and prevent harm. The goal of this project is to test how fusing clinical data, neuroimaging, and video assessments could optimise the selection of patients. The project will be in collaboration with MachineMedicine (London), a MedTech company specialising in Parkinson’s disease, and the movement disorders clinical team at St George’s Hospital, London. The goal of the collaboration is to build an app used in-clinic for patient selection. MachineMedicine are leading the app development, building on their existing app for capturing movement video in-clinic. The clinical team at St George’s are running a trial of Parkinson’s patients to acquire the essential clinical data on patient symptoms, neuroimaging (including fMRI of spontaneous brain activity), and video capture of movements. In joining this collaboration, the PhD student will be trained in data-science and machinelearning tools, including how to extract and analyse MRI and fMRI data, in fusing data across modalities, and in developing a machine-learning pipeline for predicting patient outcomes. These predictions will be tested against the 12-month follow-up data from the St George’s trial patients. The student’s further training will include a 3-month placement at MachineMedicine, and visits to St George’s clinic.
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