Reference number: FP-RG-2020
Start date of studentship: 01 July 2021
Closing date of advert: 7 January 21
Interview date: TBC
This project compares measurement accuracy between human and automated data analysis within Cell Therapy Manufacturing. This work has the potential to define standards within this industry, providing vital guidance treatment decisions to save lives. This project will give you the opportunity to work with world leading experts in biometrology and will provide many opportunities to develop your skills in healthcare engineering and bioinformatics, which are very popular career choices following this research.
Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.
Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/
Full Project Detail:
Machine vision has enormous variability in setup, use and result processing yet is prevalent for Quality Control within cell and gene therapy manufacturing. Differences in measurements have a huge impact on cell therapy treatments, as incorrect cell counting causes poor treatment decisions (false positives/false negatives), significantly impacting patient health. There is huge variety in how cells are counted, from manual counts, through to automation methods. Standards to align counting metrics would start to define traceability for cell therapy manufacturers. Machine vision and embedded intelligence are becoming more popular options to include to reduce processing times, however, the inherent variation requires definition to make better decisions for patient safety.
This project will use uncertainty principles to compare manual and automated cell counting operations, to define where and what standards can be used to reduce variability.
The successful applicant will become a member of the growing biometrology group, within the Wolfson School, and will be based in the prestigious Centre for Biological Engineering, which has a long history of innovative manufacturing research within the field of Regenerative Medicine.
Find out more: https://www.lboro.ac.uk/departments/meme/research/research-centres/cbe
Applicants should have, or expect to achieve, a strong 2:1 Honours degree (or equivalent) in Bioengineering, Mechanical Engineering, Manufacturing Engineering, Chemical Engineering, Physics, Medical Physics or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: machine vision, neural networks, cell culture, cellular imaging.
How to apply:
All applications should be made online at http://www.lboro.ac.uk/study/apply/research/
. Under programme name, select Mechanical and Manufacturing Engineering. Please quote reference number: FP-RG-2020.
Name: Dr Rebeca Grant
Email address: email@example.com
Telephone number: +44 (0) 1509 227 196