Motion Detection and Correction in Neuro MRI
MOTION DETECTION AND CORRECTION IN NEURO MRI USING RF SENSORS AND LEARNING FROM K-SPACE DATA
DUE TO FUNDING RESTRICTIONS ONLY OPEN TO HOME STUDENTS
Applications are invited for a fully-funded place on DPhil Clinical Neurosciences for the above project. This project will be a collaboration with Siemens Healthineers and is funded by an EPSRC industrial studentship.
Supervisors for this project will be:
- Dr Aaron Hess (Nuffield Department of Clinical Neurosciences)
- Prof Jared Tanner (Mathematics)
- Dr Boris Maihe (Siemens)
The student joining the project will be a member of the Wellcome Center for Integrative Neuro Imaging (WIN), part of the Nuffield Department of Clinical Neurosciences (NDCN). They will receive training through the WIN MRI graduate course - a set of courses on the theory and practice of MRI and Informal training on scanner operation and programming.
The student will also be a member of the Mathematical Institute Data Science Research Group and will take courses in and subsequently tutor the senior level courses: “optimization for machine learning”, “theories of deep learning”, and “continuous optimization.”
As a member of NDCN and the Mathematical Institute they will be imbedded in their respective graduate student support network ranging from transferable skills training, career development, psychological support, and milestones to ensure steady progression of their doctoral studies.
The studentship will cover 4 years of full fees, stipend and incidental costs, starting in October 2022.
The student will be offered internships at the Digital Technology and Innovation (DTI) lab of Siemens Healthineers in Princeton, New Jersey. The DTI lab focuses on multiple aspects of medical image processing and digital health including image acquisition, reconstruction, visualisation, computer-aided detection, and natural language processing. During their internships the student will be embedded in the Vision research group that is composed of 11 researchers and software engineers working in different areas of scanner monitoring and scan automation.
Applicants should have, or expect to gain, at least an upper second class honours degree or equivalent in Mathematics, Engineering, Physics or Biomedical Engineering.
Due to EPSRC funding restrictions, funding for this DPhil can only be awarded to a home student.
HOW TO APPLY
Applicants should apply through the formal University procedure, completing an application for this studentship on the Graduate Admissions' studentship pages, quoting studentship code: 22NEURO1WEB.
Please note that there is no need to provide a separate research proposal if you are applying for this studentship - you will only need to ensure you quote this reference when applying: 22NEURO1WEB.
The successful student will be allocated a college place at Wolfson College.