Research Associate (Machine Learning)
This is an exciting opportunity to join one of the world’s leading research centres in medical imaging: the Division of Imaging Sciences at King’s College London, which is based in St. Thomas’ Hospital in central
This post will focus on the use of novel machine learning algorithms in the problem of motion in PET-MR imaging. Specifically, the research would involve the development and/or application of dimensionality
reduction techniques to learn the inherent structure of motion from imaging data (MR and PET). A background in machine learning and/or dimensionality reduction is therefore desired.
The Division of Imaging Sciences has undergone dramatic expansion in recent years, with notable infrastructure investment including complete refurbishment of the PET centre and two new PET-CT scanners and a Siemens Biograph mMR simultaneous PET-MR scanner. Multiple key academic positions have also been created to strengthen the Division’s expertise in multi-modality imaging, comprehensively covering imaging chemistry, image acquisition, image reconstruction, analysis and clinical applications. Now with 2500 metres squared of space housing over 200 scientists, including state of the art laboratories with pre-clinical and clinical imaging systems, the Division also hosts one of four Medical Engineering Centres of Excellence funded by the Wellcome Trust and EPSRC.
This is an excellent opportunity for an ambitious researcher to join this stimulating environment and perform cutting edge research into the application of machine learning to the problem of motion in PET-MR. The
person appointed to the role will be expected to work under the supervision of the grant principal investigator, Dr A. King, and in collaboration with colleagues at King’s. The work will involve developing novel techniques, publishing in high impact journals and presenting findings at international conferences. It is anticipated that
a number of high profile publications would result from the planned research.
Please contact email@example.com for informal discussions and details of the application process, or visit
https://www.hirewire.co.uk/HE/1061247/MS_JobDetails.aspx?JobID=76345 to apply.