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The biomechanical mechanism of sports-related concussion

  • Full or part time
  • Application Deadline
    Friday, April 24, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

There is a growing body of evidence regarding the risk of several neurodegenerative diseases associated with sports-related concussion (SRC). The development of better technologies and systems to prevent and/or mitigate the severity of SRC requires an improved understanding of the injury mechanisms. Accordingly, the aim of this multimodal project involves elite-level rugby athletes, in-vivo head kinematics, computational biomechanics, and biomarkers to identify the underlying biomechanical injury mechanism of SRC.
In brief, Impact Monitoring Mouthguards (IMM) will be used to measure in-vivo 3D head kinematics during SRC events. Furthermore, finite element brain models will be developed to understand the deformation response of the brain to SRC head trauma. The aim is to develop SRC risk models that link head-impact kinematics to finite element brain model outputs and/or clinical biomarkers to aid side-line medical staff in removing concussed players from the field in the future. Additionally, the field data protocol will be used to quantify the severity and identify the main causes of SRC and head accelerations in rugby to guide future prevention strategies.
Candidates should have:

Funding Notes

This project is funded for 3.5 years. Tuition fees (Home/EU rate) and a stipend equivalent to the Research Council rate will be provided.

Candidates should have:
• Expected 2:1 or higher in either Engineering, Computer Science, Physiotherapy, Physics or Sports Science/Medicine Degree
• Passion for Sports Biomechanics and Engineering

Ideally candidates will have:
• Experience working with wearable technology, Finite Element Analysis, programming (e.g. Matlab/Python) and/or predictive modelling
• Experience working with sports teams

How good is research at University of Leeds in Biological Sciences?

FTE Category A staff submitted: 60.90

Research output data provided by the Research Excellence Framework (REF)

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