Ready to make your mark in antimicrobial research (AMR)?
Looking for an opportunity that nurtures research excellence, real-world impact and professional development?
This PhD opportunity is for you!

In partnership with Solving Antimicrobial Resistance in Agribusiness, Food and the Environment (SAAFE) Cooperative Research Centre (CRC) and The University of Queensland, this PhD focusses on developing the SAAFE CRC Digital Twins for Risk Assessment Modeling in AMR research.

Enrolment status - Future UQ student, Current UQ student

Student type - Domestic

Study level - Postgraduate research (HDR)

Study area - Agriculture and animal sciences, Computer science and IT, Environment

Scholarship focus-Academic excellence

Funding type – Base stipend + Top-up

Scholarship value - $45,000 per annum, $21,000 professional development fund

Scholarship duration - Up to 3.5 years

About this scholarship

Supervisor: Dr Noorul Amin

Associate supervisor: Professor Ricardo Soares Magalhaes

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 allows 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.

Eligibility

You're eligible if you meet the entry requirements for a higher degree by research.

Applicants must hold an Honours degree as the minimum education requirement for this PhD opportunity.

This opportunity is open to domestic students only.

How to apply

To be considered for this scholarship, please email the following documents to Dr Noorul Amin ([email protected]):

Cover letter

CV

Academic transcript/s

Evidence for meeting UQ's English language proficiency requirements eg TOEFL, IELTS

2-3 page research proposal relevant to this project

Please note the following: Submitting the above documents does not constitute a full application for admission into The University of Queensland's PhD program. If you are selected as the preferred applicant, you will then be invited to submit a full application for admission. You can familiarise yourself with the documents required for this process on the UQ Study website.

Selection criteria

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.

Rules

Read the policy on UQ Research Scholarships.

A domestic part-time student with carer's responsibilities, a medical condition or a disability, which prevents them from studying full-time may be eligible for scholarship consideration, on a case-by-case basis.

Apply now!