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Research Fellow: Machine Learning for Multi-scale, Correlative, Biomedical Imaging

UCL

Location: London
Salary: The salary range will be £36,770 - £44,388 per annum, inclusive of London Allowance
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 21st April 2022
Closes: 3rd June 2022
Job Ref: 1883683
 

Fixed Term: The post is available from now till 31th October 2024 in the first instance.

The appointment will be on UCL Grade 7.

UCL is seeking to appoint a Research Fellow to apply Machine Learning techniques to correlate a new and disruptive biomedical imaging modality – Hierarchical Phase-Contrast Tomography (HiP-CT), to existing clinical imaging modalities including CT, MRI and histology, providing ground truth for super-resolution MRI techniques. HiP-CT is an ex vivo X-ray imaging technique developed at the European Synchrotron Radiation Facility in Grenoble, capable of multi-resolution imaging of intact human organs. With HiP-CT we are able to image whole human organs with 25um voxels then zoom down to near single cell resolution anywhere within the organ without physically cutting the sample (bit.ly/HiP-CT-videos, mecheng.ucl.ac.uk /HiP-CT, bit.ly/HiP-CT-paper).

The Research Fellow will be based in Bloomsbury London, in the Mechanical Engineering Department but working closely with UCL Computer Science department. The Fellow will lead the development of new ML based image processing pipelines to correlate HiP-CT images to clinically used modalities e.g. MRI, CT and histology. The post-holder will devise deep-learning based workflows, extracting biomedical data from HiP-CT images and correlating these with imaging biomarkers from lower resolution clinical imaging modalities to obtain super-resolution.  The post is available from now till 31th October 2024 in the first instance.

The post holder will have a PhD and extensive knowledge and expertise in a relevant field, experience with open source machine learning libraries and handling large image datasets are essential, experience with multimodal datasets is desirable. Your expertise should be at a level appropriate for the conduct of research and publishing new knowledge in leading international research journals. The post-holder will need to show a high level of initiative and an ability to work collaboratively and independently. Applicants should have good team-working skills and a strong command of English. Ideally, you would have a proven track record in correlative imaging, machine learning and large data image analysis.

If you have any questions about this role please contact Ruikang Xue, Project Manager, Mechanical Engineering - ruikang.xue@ucl.ac.uk.

Interview Date: TBC

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