Advancing the visualisation and quantification of nephrons with MRI

Project opportunity

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Nephrons are the basic functional unit of the kidney, an organ with a central role in maintaining homeostasis in the body. The number of nephrons in the kidneys and their microstructure reflect the success of renal development and the trajectory of renal health through life. Low nephron number increases the risk of chronic kidney disease, hypertension and cardiovascular disease.

Current methods for nephron quantitation are limited to ex vivo methods which are labour intensive, affected by shrinkage or use contrast agents. Magnetic Resonance Imaging (MRI) has strong potential to characterise kidney microstructures, but in vivo it suffers from low image resolution and motion artefacts. This PhD project aims to develop novel methods for kidney MR image acquisitions and analyses using artificial intelligence (such as Deep Learning and super resolution methods) to allow characterisation of key components of nephrons, the glomeruli and tubules. It is expected that these new methods will play important roles in future kidney research and contribute to reducing Australia’s epidemic of chronic kidney disease.

This is an opportunity to work with researchers at the Centre for Advanced Imaging, a leading imaging research facility in Australia, and The University of Queensland School of Information Technology and Electrical Engineering.

The project will suit an enthusiastic and highly motivated student with a background in computer science, physics or engineering.

Scholarship value

As a scholarship recipient, you'll receive: 

  • living stipend of $32,192 per annum tax free (2023 rate), indexed annually
  • tuition fees covered
  • single Overseas Student Health Cover (OSHC)


Professor David Reutens

Centre for Advanced Imaging


Preferred educational background

Your application will be assessed on a competitive basis.

We take into account your

  • previous academic record
  • publication record
  • honours and awards
  • employment history.

A working knowledge of deep learning applied to the analysis of images, particularly magnetic resonance images would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of computer science, physics, engineering and the potential for scholastic success.

A background or knowledge of magnetic resonance imaging is highly desirable.

Latest commencement date

If you are the successful candidate, you must commence by Research Quarter 2, 2023. You should apply at least 3 months prior to the research quarter commencement date.

If you are an international applicant, you may need to apply much earlier for visa requirements.

How to apply

You apply for this project as part of your PhD program application.

View application process