Ecoacoustics with Machine Learning

About the research project

Ecoacoustics is the application of bioacoustics recording and analysis technologies to answer ecological questions. It offers a non-intrusive method for monitoring vocalising species in natural habitats, including birds, frogs, bats and other megafauna. However, most monitoring processes require the deployment of recorders for relatively short periods of time (several weeks), after which the recording must be collected from the field. This makes it difficult to monitor some species, particularly activities relating to breeding. This project will contribute to the next phase in ecoacoustics: onboard analysis of captured audio data with detections transmitted over a LoRaWAN network. This is a multifaceted problem, spanning hardware, machine learning, software and ecological practice. The main focus of this project, based in the discipline of Mathematics, will be on the creation and optimisation of low-size machine learning classifiers, for long-term deployment on independently operating devices deployed for periods of months or more. It will explore and optimise the end-to-end onboard processing of acoustic signals for targeted monitoring applications (small number of species or specific behaviours), in particular for monitoring breeding activity of iconic species (swift parrots, wedge-tailed eagles), as well as activity by sugar gliders (a key threat to swift parrots), Tasmanian masked owls; and mainland species including koalas and yellow-bellied gliders. Indicators of illegal wood harvesting (e.g., chainsaws) are also an active target of interest. The work will be conducted in collaboration with Sustainable Timber Tasmania and other partner organisations, drawing on their extensive libraries of forest acoustic recordings and expertise in forest management, and is a key complementary project to more hardware-focused PhD project in ICT at UTAS. This project is fully funded by a grant from Forest & Wood Products Australia to Dr James Montgomery & Dr Saurabh Garg (ICT), Dr Ananda Maiti (ICT) and Prof Michael Charleston (Mathematics).

Primary supervisor

Meet Professor Michael Charleston

Funding

The successful applicant will receive a scholarship which provides:

• a living allowance stipend funded by Forest & Wood Products Australia Limited of $34,315 per annum for 3.5 years
• a relocation allowance of up to $2,000
• 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:

• Applicants must be able to undertake the project on-campus

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:

• Applicants should be well versed in statistics / linear algebra and fundamental concepts of machine learning.

Additional desirable selection criteria specific to this project:

• Ability to program in a high-level language is highly desirable. Competence in C, Python and R will be particularly valued. A background in data analysis and/or ecology are highly desirable.

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Professor Michael Charleston 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 April 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.

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