Do you have a strong technical background in computational mechanics with an interest in biomedical engineering? Would you like to work as part of a multidisciplinary team to address a clinically-driven challenge?    

This project is part of a major £4M EPSRC Programme Grant on Optimising Knee Therapies. The aim of the programme is to develop preclinical testing methods for early-stage treatments for knee osteoarthritis so their performance can be optimised. 

In the UK, one third of people aged over 45 have sought treatment for osteoarthritis. The knee is the most common site for osteoarthritis and there is a major unmet clinical need for effective earlier stage interventions that delay or prevent the requirement for total knee replacement surgery. 

The aim of this project is to use methods that have been previously developed to generate computational models of the human knee. The models will be used to evaluate a range of treatments to the cartilage and meniscus. You will join a team that have developed image-based finite element models of the tibiofemoral joint. Specifically, your role will be to develop finite element models from 3D image data, using bespoke software and novel processes developed in-house. 

You will have a strong background in finite element analysis related to tribology or material interfaces and contact mechanics, and have a proactive approach to working in a multidisciplinary team with engineers, biologists and clinicians.  

To explore the post further or for any queries you may have, please contact:

Marlène Mengoni, Lecturer
Tel: +44 (0)113 343 5011
Email: m.mengoni@leeds.ac.uk 

Location: Leeds - Main Campus
Faculty/Service: Faculty of Engineering & Physical Sciences
School/Institute: School of Mechanical Engineering
Section: Institute of Medical and Biological Engineering
Category: Research
Grade: Grade 6
Salary: £27,511 to £32,817 p.a.
Post Type: Full Time
Contract Type: Fixed Term (Until 30 September 2021 (Grant funding))
Closing Date: Friday 17 July 2020
Reference: EPSME1024
Downloads: Candidate Brief