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Job Opportunity: PhD Studentship: Towards diagnostic quality low-field brain MRI using machine learn:

Job description:

Qualification Type: PhD
Job location: Brighton
Funding for: UK Students, EU Students, International Students
Funding amount: £16,062 16062
Hours: Full Time

Published in: 30th June 2022
Closing date: 11th July 2022

 

What you get
You will receive a tax-free stipend at a standard rate of £16,062 per year for 3.5 years and a research training grant of £2,000 for the 3.5-year duration. In addition, your fees will be waived for 3.5 years up to the UK, EU, orInternationalrate.
PhD project
Low-field MRI, operating at magnetic field strength two orders of magnitude lower than commercial clinical scanners, offers excellent potential for bringing cheap, sustainable and effective imaging to regions and countries where MRI is scarce or unavailable. This field of research is rapidly growing and initial clinical usage of these devices, in specific situations, is becoming a reality. However, these low-field systems suffer from a number of technical limitations that prevent widespread adoption, in particular: images have low contrast between different tissue types; poor signal-to-noise ratio; increased geometric distortions; and if unshielded, suffer strongly from interference artefacts.
This projectwill addresssome of thesechallenges in low-field brain MRI through the development of novel image analysis and machine learning methods. The overall project aim is to produce images and derived measurements that contain robust clinically useful information from low-field MRI. As large low-field datasets are unavailable, this work will focus on working with small ?paired? high- and low-field datasets and simulating low-field MRI, from existing large-scale high-field datasets.
This is a computational project, which would suit a student with good mathematical and programming expertise and a keen interest in medical imaging. Students will be expected to present their work at top-tier medical image analysis, computer vision or machine learning venues such as MICCAI, IPMI, CVPR, ICCV/ECCV, MICCAI,NeurIPSetc.
This project is jointly supervised byDr Ivor Simpson, a lecturer in thePredictive Analytics Labwithin theAI research groupat theUniversity of Sussexand ProfessorItamar Ronenthe academic director of theClinical Imaging Sciences CentreatBrighton and Sussex Medical School. Both institutionsare situatedwithin the great south coast city ofBrighton and Hove,adjacent to the beautiful South Downs national park.
Eligibility
Eligible candidates will have recently received a good Masters qualification and/or a first or high 2:1 undergraduate in a significant subject or have substantial recent practice working in a related field.
Deadline
11 July 2022 0:00
How to apply
Apply online for a full time PhD in Informatics using our step-by-step guide (http://www.sussex.ac.uk/study/phd/apply). Here you will also find details of our entry requirements.
Please clearly state on your application form that you are applying for theTowards diagnostic quality low-field brain MRI using machine learningScholarship, under the supervision of Dr Ivor Simpson.
Interested candidates may contact Dr Simpson directly prior to applications. All candidates should include a Statement of Academic Interests, explaining their specific interests in the research topic and their significant expertise.
Contact us
Contact Us by phd.informatics@sussex.ac.uk with any questions related to the admissions process.
Interested candidates may contact Dr Simpson directly prior to applications. All candidates should include a Statement of Academic Interests, explaining their specific interests in the research topic and their significant expertise.
Timetable
Application deadline: 11/07/2022
Interview date: 15/07/2022
Notification date: 20/07/2022
Start date: 19/09/2022

Skills:

Job Category:  [ View All Jobs ]
Language requirements:
Employment type:
Salary: Unspecified
Degree: Unspecified
Experience (year): Unspecified
Job Location: Brighton, East Sussex England
Address: East Sussex
Company Type Employer
Post Date: 06/30/2022 / Viewed 1 times
Contact Information
Company:
Contact Email: phd.informatics@sussex.ac.uk


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