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SAAFE CRC Digital Twins for Risk Assessment Modelling

This project is closed.

Project summary

Program
PhD
Location
Gatton
Research area
Agricultural, veterinary and food sciences, Environmental sciences, Information and computing sciences

Project description

Antimicrobial resistance (AMR) poses a major threat to animal health and food security, requiring robust risk assessment models for effective management in agribusiness, food and environmental sectors. However, data in these sectors is fragmented, limited, and privacy-sensitive, hindering model development. Remote farms further complicate data collection due to technological limitations.

Realistic but synthetic data that mirrors the statistical properties of real-world agribusiness AMR data can fill the gaps of comprehensive and accurate data availability, ensure privacy preservation, and resolve the technological limitation of remoteness. The synthetic AMR data will enable the parameterisation of robust and enhanced risk assessment models and allow them to be tested and validated on a wider range of use cases, scenarios, and sectors. Additionally, the use of synthetic data is cost-effective, reducing the requirements of extensive and expensive data collection efforts. This will lead to enhanced, more accurate risk assessment and informed decision-making.

Scholarship

This project is supported by the Research project scholarship.

This scholarship includes:

  • living stipend of $45,000 per annum
  • funding package of $21,000 for professional development
  • tuition fees covered.

This scholarship includes:

  • living stipend of $45,000 per annum
  • funding package of $21,000 for professional development
  • tuition fees covered.

Learn more about the Research project scholarship.

Supervisor

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 machine learning, data analysis, and analytical skills would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field/s of machine learning and the potential for scholastic success.

A background or knowledge of data analysis and machine learning is highly desirable.

How to apply

You must submit an expression of interest (EOI) by 28 November, 2025 28 November, 2025.

Before you apply

  1. Check your eligibility for the Doctor of Philosophy (PhD).
  2. Prepare your documentation.
  3. If you have any questions about whether the project is suitable for your research interests, contact Dr Noorul Amin (noorul.amin@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

  1. Are you applying for an advertised project: 'Yes'
  2. Project: 'Research project scholarship'
  3. Scholarship Code Listed in the Advertisement: DIGITAL TWINS-AMIN
  4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/saafe-crc-digital-twins-risk-assessment-modelling

Submit an EOI

This project is not available to international students

Do you have questions?

The Graduate School is here to help.

Contact us