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Research Fellow in MR Physics and Machine Learning

  • Application Deadline
    07 February 2020
  • Job Salary
    £35,965 to £41,165 Inclusive of London Allowance.
  • Website
  • Contact Name
    Professor Muthurangu

The appointment will be on UCL Grade 7. The salary range will be £35,965 - £41,165 per annum, inclusive of London Allowance.

This research appointment, based at the UCL Institute of Cardiovascular Science (Centre for Cardiovascular Imaging) and Great Ormond Street Hospital, will work on the project Machine Learning for Rapid Cardiovascular Magnetic Resonance in Children, funded by the British Heart Foundation.

The group has recently developed machine learning based reconstructions that are able to produce better quality images in less time. The post-holder will be responsible for building on these technologies, including investigating novel neural network architectures and iterative network algorithms for different types of rapid CMR imaging as well as image post processing.

The appointment is funded until 31 May 2021 in the first instance.

Candidates will have a PhD in computer science, mathematics, engineering or physics; if the degree has not yet been awarded, the final accepted version of the thesis should have been submitted to the degree awarding university by the start date. They will also have experience in the use of machine learning methods and significant experience in a programming language eg C/C++, Python, Matlab. Any practical knowledge of image reconstruction and MRI pulse programming would be desirable.

For further details about the vacancy and how to apply online please go to https://www.ucl.ac.uk/human-resources/working-ucl/jobs-ucl and search on Reference Number 1849885.

If you have any queries regarding the vacancy please contact Professor Muthurangu ( ); for queries on the application process please contact .

Closing Date: 7 Feb 2020.

Latest time for the submission of applications: 23.59.

Interview Date: TBC.

UCL Taking Action for Equality.

We will consider applications to work on a part-time, flexible and job share basis wherever possible.

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