Enhancing clinical brain MRI scans with deep learning for improved deep brain stimulation (23011)

4-year PhD studentship - Enhancing clinical brain MRI scans with deep learning for improved deep brain stimulation - NOW CLOSED

Primary Supervisor:  Prof Gary Zhang
Secondary Supervisors: Dr Harith Akram, Prof John Ashburner , Dr Christian Lambert

Introduction:

A 4-year year funded PhD studentship is available jointly at the UCL Centre for Medical Image Computing Department of Computer Science, the Unit of Functional Neurosurgery, and the Wellcome Trust Centre for Neuroimaging. Funding is from the Engineering and Physical Sciences Research Council and the Brain Research Trust and it will be at least the UCL minimum. Stipend details can be found here.
 

The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort and benefit from the activities and events organised by the centre.

Project Background:

Deep brain stimulation (DBS) is an established therapeutic technique in the treatment of several neurological and neuropsychiatric disorders.  However, the success of DBS procedures hinges on the accurate identification of the surgical target.  In research settings, high-quality MRI sequences have been demonstrated to significantly improve our ability to identify surgical targets over the use of standard clinical sequences.  However, the reliance on high-quality sequences has limited the extent to which these techniques have been translated into clinical practice.

Research aims:

The main aims of this project are 1) to develop advanced AI algorithms that can leverage high-quality MRI scans acquired in the research settings to enhance routine clinical MRI scans, and 2) to apply the resulting algorithms to improve the accuracy in identifying the surgical target of DBS with MRI scans acquired in a clinical setting.

Person specification & requirements:

• Applicants are expected to have achieved (or are predicted) a first-class or upper second-class honours undergraduate degree (or equivalent international qualifications or experience). An MSc is also preferred, though not essential.
• Our preferred subject areas are Physical Sciences (Computer Science, Engineering, Mathematics and Physics). Applicants with degrees in Chemistry or Life Sciences are welcome but applicants must be able to demonstrate strong mathematical skills.

A full studentship is available for home fee payers only. UCL’s fee eligibility criteria can a be found by following this link.

Application Deadline: 14^th July 2023

How to apply:

Please complete the following steps to apply.

• Send an expression of interest and current CV to: gary.zhang@ucl.ac.uk and cdtadmin@ucl.ac.uk . Please quote Project Code: 23011 in the email subject line.
• Make a formal application to via the UCL application portal https://www.ucl.ac.uk/prospective-students/graduate/apply . Please select the programme code Medical Imaging TMRMEISING01 and enter Project Code 23011 under ‘Name of Award 1’

 

Application Process:

• After the deadline, all applicants that specified Project 23011 and with a Portico application will be considered for interview.
• Candidates will normally be invited for interview within two-weeks of the deadline. If you have not been contacted within this time-period, you have unfortunately not been successful in being shortlisted.
• The interview panel will normally consist of the supervision team on the project and the CDT Director.
• The interview will normally consist of a short presentation (5-10mins) by the candidate followed by questions from a panel.
• The successful candidate will be informed by email and given a week to confirm whether they wish to accept the PhD place and funding.
• Note that applications without specifying the project they are applying for and/or making a formal Portico application will be automatically rejected.
• Once accepted, a formal UCL offer of admission will be sent to the applicant as well as an offer of studentship funding.