Scholarship details

Student type

Future students

Study area

Engineering, Health and community, Science, technology and engineering and mathematics

Citizenship

Australian or New Zealand and International

What you'll receive

You'll receive:

  • a living allowance of AU$28,597 (2021 rate, indexed annually) for up to 3 years if you're a full-time PhD student.

International students will also be considered for a tuition fee sponsorship.

Eligibility

You must:

  • meet QUT academic and English language entry requirements for the IF49 Doctor of Philosophy (PhD)
  • hold an undergraduate degree with first or second class division A honours, or a masters degree by research in mechanical or biomedical engineering, physics, or a related discipline.

Desired skills:

  • You should have an interest in working both experimental and computational engineering. Experience in FE modelling and simulation, biomechanics, artificial intelligence and machine learning, biomedical sciences, and physiology is desirable.

The provision of a scholarship is conditional on successful application and admission to the IF49 Doctor of Philosophy. Eligibility for admission to a research degree is determined by the Graduate Research Centre.

How to apply

Email the following to Maddie McIntyre at maddie.mcintyre@qut.edu.au:

  • an updated CV
  • academic transcripts
  • outline of your research interests.

Applications will be accepted until the scholarship is awarded, but applications will be assessed starting from 15 August 2021.

What happens next?

  • If successful, you will be invited to apply and must be accepted into QUT's Doctor of Philosophy program to receive this scholarship.

How to apply for a research degree

Conditions

About the scholarship

Three PhD scholarships are available at the Queensland Unit for Advanced Shoulder Research (QUASR), at the Queensland University of Technology (QUT), Brisbane, Australia. The projects are part of a collaboration between QUASR and the Herston Biofabrication Institute (HBI), Metro North Hospital & Health Service.

QUASR is a multidisciplinary, transformational research centre, promoting and conducting dedicated research within the realm of orthopaedic shoulder surgery based at the Queensland University of Technology (QUT) in Brisbane, Australia. HBI is the first institute of its kind – advancing knowledge and technology in 3D scanning, modelling, and printing of medical devices, bone, cartilage, and human tissue. The Institute takes a multidisciplinary approach, bringing together clinicians, academics, industry, and consumers to transform how healthcare is provided by developing innovation and automated treatments.

Three PhD scholarships are available to work on improved understanding of common factors impacting revision rates in reverse shoulder arthroplasty (RSA). The successful candidates will be using advanced imaging and image processing techniques, multi-scale and FE modelling, and artificial intelligence algorithms to assess and optimise implant geometry, mechanical stability, and impacts of common pharmaceuticals on bone quality and stock. The successful candidates will each work on a specific project, as outlined below, but will contribute towards a common goal of improving surgical outcomes and reducing (re-) revision rates in shoulder surgery.

Research projects

Send general enquiries to Dr Deniz Erbulut at Deniz.Erbulut@health.qld.gov.au. Project-specific enquiries should be emailed to the principal supervisor.

Optimisation of glenoid base plate fixation in reverse shoulder arthroplasty

Aseptic loosening of the glenoid component is a common complication in reverse shoulder arthroplasty (RSA) that has been attributed to a complex interaction between bone quality and distribution, loading, implant geometry and surgical placement. The aim of this project is to optimize the geometry of RSA implants and their position for minimizing the risk of aseptic loosening. The implant geometry and surgical procedure will be optimised for a representative cohort of patients using image-based models of implant stability.

Principal supervisor:
Associate Professor Saulo Martelli (QUT)
Associate supervisors
Dr Deniz Erbulut (HBI)
Professor Kevin Tetsworth (HBI)
Professor Ashish Gupta (Adj)
Associate Prof Ken Cutbush (QUT)

For project enquiries, email Associate Professor Saulo Martelli.

Biomechanical investigation of revision reverse shoulder arthroplasty surgery using FEA and AI algorithms

Revision surgery following reverse total shoulder arthroplasty (RSA) is a complex surgical procedure showing higher failure rates than primary RSA due to complexity and variability of the residual bone after explant. The aim of this project is to quantify the mechanical stability for a representative set of revision surgery patients and develop techniques to minimise the risk of mechanical failure of revision surgery implants.

The project will use personalised finite-element technologies and AI algorithms to relate the bone quality and outcomes of the revision RSA patients.

Principal supervisor
Professor YuanTong Gu (QUT)
Associate supervisors
Dr Deniz Erbulut (HBI)
Professor Kevin Tetsworth (HBI)
Professor Ashish Gupta (Adj)
Associate Prof Ken Cutbush (QUT)

For project enquiries, email Professor YuanTong Gu.

Effects of bone tissue quality on shoulder implant stability in reverse shoulder arthroplasty

Risk factors for the success of RSA surgeries are age, genetics, bone quality, and loading. While many patients are treated with osteoporosis (OP) drugs to promote bone quantity and quality before RSA, the impact these drugs have on bone matrix properties and surgical outcomes is not well understood. The aim of this project is to quantify the effect of common osteoporosis treatment drugs on bone tissue quality and capacity to provide support to RSA implants.

The project will investigate the macroscopic and tissue level bone structure within the shoulder using CT and micro-CT imaging. Finally, at the microscopic scale we will use quantitative backscattered electron imaging (qBEI) to analyse bone tissue mineral density distribution on the scale of trabeculae. This data will serve to build a multiscale model of bone stiffness to assess the effects of drug treatments on implant stability.

Principal supervisor
Professor Peter Pivonka (QUT)
Associate supervisors
Dr Deniz Erbulut (HBI)
Professor Kevin Tetsworth (HBI)
Professor Ashish Gupta (Adj)
Associate Prof Ken Cutbush (QUT)

For project enquiries, email Professor Peter Pivonka.

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