SAAFE CRC Digital Twins for Risk Assessment Modeling Top-Up Scholarship
This scholarship is closed.
- Enrolment status
- Future UQ student, Current UQ student
- Student type
- Domestic, International
- Study level
- Postgraduate research (HDR)
- Study area
- Agriculture and animal sciences, Computer science and IT, Environment
- Scholarship focus
- Academic excellence
- Funding type
- Top-up
- Scholarship value
- $15,000 per annum
- Scholarship duration
- Up to 3.5 years
- Number awarded
- May vary
- Applications open
- 13 February 2025
- Applications close
- 10 April 2025
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.
You're eligible if you have secured a base living stipend. Alternatively, you are considering to apply for next Graduate School Scholarships (UQGSS) round or other base living stipend scholarships.
How to apply
To be considered for this scholarship, please email the following documents to Dr Noorul Amin (noorul.amin@uq.edu.au):
- 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.