Investigating the contribution of meniscal roots to human knee joint contact mechanics: Applying machine learning, experimental and modelling approaches.

Description

NTHU-UoL Dual PhD Programme between National Tsing Hua University in Taiwan and the University of Liverpool in the UK is a well-established programme, where students spending 2 years at both institutions. Working with world leading academics and research capabilities the PhD candidates will spend two years in each institution. Upon successful defence of their research work, the candidates will obtain dual PhD degrees.

Knee joint menisci are crucial components of human knee joints as they act as shock absorbers by converting the vertical compressive loads into circumferential stress, commonly known as hoop stress [1]. Unfortunately, knee meniscal injuries, including meniscal root tears, are one of the most common orthopaedic injuries worldwide. Meniscal roots are ligament-like structures that anchor knee menisci to the tibia, maintain meniscal hoop tension and prevent meniscal extrusion. When these roots are damaged due to degenerative disease or traumatic events, their mechanical properties change, causing alteration in the joint function. However, the mechanism behind this alteration is not clear. This project aims to develop computational and experimental methods to better understand the consequences of meniscal root injuries. The objectives include, applying machine learning to develop a constitutive material model and developing a computational modelling framework combined with the determined material models. The student will be delivering the aim of the research project under the supervision of two supervisors, an expert in tissue mechanics (Dr Readioff from UoL) and machine learning expert (Dr Huang from NTHU).

This project will require the student to spend 2 years at NTHU followed by 2 years at UoL. A successful candidate have an interest in bioengineering, a basic understanding of the finite element method for solid mechanics, fundamental computer coding skills (especially in Python/C++) and some knowledge on human anatomy. Experience with PyTorch, TensorFlow, Scikit-Learn, or other well-known machine learning packages is desirable but not essential as training will be provided. Interested applicants may email Dr Tsung-Hui Huang at thhuang@mx.nthu.edu.tw (NTHU) and Dr Rosti Readioff at rosti.readioff@liverpool.ac.uk (UoL) for more information.

We want all of our staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, If you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.

We believe everyone deserves an excellent education and encourage students from all backgrounds and personal circumstances to apply.

Applicant Eligibility

Candidates will have, or be due to obtain, a Master’s Degree or equivalent from a reputable University in an appropriate field of Engineering. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered.

Application Process

Candidates wishing to apply should complete the University of Liverpool application form [How to apply for a PhD - University of Liverpool] applying for a PhD in Materials Engineering and uploading: Degree Certificates & Transcripts, an up-to-date CV, a covering letter/personal statement and two academic references.