3D total body photography with deep imaging phenotyping for objective melanoma risk prediction

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.

Melanoma is one of Australia’s most common cancers, but it is still difficult to predict who will develop melanoma. While the risk factors for melanoma are well documented, measuring them is often time-consuming, as it is highly subjective. This project will develop and validate an automated and objective individualised melanoma risk prediction tool from non-invasive 3D total body photography. It will incorporate known phenotypic risk factors including naevus phenotype, skin colour, UV damage, and hair/eye colour, which will be automatically extracted from the 3D images using machine learning.

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 Peter Soyer

Faculty of Medicine

Email: p.soyer@uq.edu.au

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 total body imaging would be of benefit to someone working on this project.

A background or knowledge of medical imaging, skin cancer risks and risk scoring is highly desirable.

Latest commencement date

If you are the successful candidate, you must commence by Research Quarter 1, 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