PDRF in Machine learning Multi-scale, Correlative, Bio-Imaging

About us

University College London (UCL)

UCL is a multi-disciplinary university with a population of over 13,000 staff and 42,000 students from 150 different countries. Degree programmes are provided in Arts and Humanities, Built Environment, Brain Sciences, Engineering Sciences, Education, Laws, Life Sciences, Mathematical & Physical Sciences, Medical Sciences, Population Health Sciences and Social and Historical Sciences. For more information, please visit http://www.ucl.ac.uk/about

UCL Mechanical Engineering

UCL Mechanical Engineering has been pioneering the development of engineering education, having taught the core discipline for over 165 years. UCL was home to the UK’s first ever Professor of the Mechanical Principles of Engineering, Eaton Hodgkinson, in 1847. It was also where Sir Alexander Blackie William Kennedy introduced organised laboratory practicals in university education training; a world-leading educational innovation at the time.

About the role

UCL is seeking to appoint a Research Fellow to apply Machine Learning techniques to correlate a new and disruptive bio-medical 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.

Please note, this post is funded for two years in the first instance.

About you

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 will have a proven track record in correlative imaging, machine learning and large data image analysis.

The successful candidate will join a dynamic international multidisciplinary group of academics, clinicians, beamline scientists, post-docs and PhD students developing and applying synchrotron X-ray and other techniques to study biological systems.

Please see the job description and person specification for further information.

What we offer

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