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Position: Research Fellow: Machine learning for Multi-scale, Correlative, Biomedical Imaging
Institution: University College London
Department: Mechanical Engineering
Location: London, United Kingdom
Duties: 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
Requirements: The post holder will have a PhD and extensive knowledge and expertise in a relevant field, experience with open source machine learning libraries and handelling 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
   
Text: Research Fellow: Machine learning for Multi-scale, Correlative, Biomedical Imaging, - Ref:1883683 Click here to go back to search results Apply Now UCL Department / Division Mechanical Engineering Location of position London Grade 7 Hours Full Time Salary (inclusive of London allowance) £36,770 - £44,388 per annum Duties and Responsibilities 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. Key Requirements The post holder will have a PhD and extensive knowledge and expertise in a relevant field, experience with open source machine learning libraries and handelling 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. Further Details A job description and person specification can be accessed at the bottom of this page. To apply for the vacancy please click on the Apply Now button below. If you have any questions about this role please contact Ruikang Xue, Project Manager, Mechanical Engineering - ruikang.xue@ucl.ac.uk. UCL Taking Action for Equality We will consider applications to work on a part-time, flexible and job share basis wherever possible. Closing Date 12 May 2022 Latest time for the submission of applications 23.59 Interview date TBC This appointment is subject to UCL Terms and Conditions of Service for Research and Support Staff. Please use these links to find out more about UCL working life including the benefits we offer and UCL Terms and Conditions related to this job. Job description and person specification Apply Now
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