Species-Selective deep-learning to Monitor and Mitigate Devil Facial Tumour Disease

About the research project

Conventional live trapping of Tasmanian devils is essential for monitoring the spread of Devil Facial Tumour Disease (DFTD) and will remain necessary for evaluating vaccine deployment. However, trapping is labour-intensive and often inefficient. In areas with high devil density, traps repeatedly capture the same individuals. In areas with low density, traps frequently capture non-target species (e.g., quolls). These limitations reduce the amount of data researchers can collect while causing unnecessary stress to devils and other native animals.

We will develop a next-generation live trap integrating a microchip scanner that prevents previously captured devils from being retrapped. The trap will also incorporate a camera and onboard artificial intelligence to selectively capture Tasmanian devils while excluding other species. This technology will substantially improve our ability to determine the distribution and impact of DFT2 and strengthen monitoring of vaccine safety and effectiveness.

In this project you will learn deep learning, general programming, electronics and machinal design.

An ideal candidate will have a background in programming. Familiarity with basic electronics or deep learning is a bonus, but not a requirement. An interest in the outdoors or ecology is desirable, as this project will require some field work.

The project will be supervised by Dr William Connelly (william.connelly@utas.edu.au). We will be working closely with the Wild Immunity group of Prof Andy Flies

Project funds are generously provided by the Dr Eric Guiler Tasmanian Devil Research Grant.

Primary supervisor

Meet Doctor William Connelly

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

• a living allowance stipend funded by University of Tasmania of $34,315 per annum for 3.5 years
• a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

A tuition fee offset may be offered to eligible international applicants following competitive assessment 

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees for undertaking a research degree, please visit our Scholarships and fees on research degrees page.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Ensure your eligibility for the scholarship round by referring to our Key Dates.

Additional eligibility criteria specific to this project/scholarship:

• Applications are open to Domestic/International/Onshore applicants

Selection criteria

The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

• Experience in Programming (Python, C/C++, R, Matlab)

Additional desirable selection criteria specific to this project:

• Experience in Electronics
• Experience in Machine learning
• An interest in the outdoors

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Doctor William Connelly to discuss your suitability and the project's requirements; and
3. In your application:
   • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
   • Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
4. Apply prior to 1 October 2026.

Full details of the application process can be found under the ' How to apply ' section of the Research Degrees website.

Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

Apply nowExplore other projects