Generative graph modelling for anomaly detection
This scholarship offers an excellent opportunity to develop an international research profile and contribute to impactful, cutting-edge work at the forefront of graph anomaly detection.
We are offering two fully funded international PhD scholarships to join our exciting research project funded by the Australian Research Council (ARC): “Generative Graph Modelling for Anomaly Detection.”
Key details
- 20268166, 20268167
- PhD
- Applications close on 30 April 2026
- International
- Information technologies
- $39,700 p.a. (2026 rate)
The project aims to develop novel generative approaches for modelling complex graph-structured data and detecting rare, evolving anomalies. The research is expected to produce high-quality outcomes with strong theoretical and empirical contributions, targeting leading venues in data mining, machine learning and artificial intelligence, such as:
- KDD
- ICDM
- WWW
- IJCAI
- AAAI
- ICLR
- NeurIPS
- EMNLP
- ACL
- TKDE
- TNNLS
- TPAMI.
We are seeking self-motivated and research-oriented candidates with:
- strong foundations in graph-based mining and learning
- solid mathematical training
- hands-on research experience.
Availability
This scholarship is available to eligible candidates to undertake a direct entry three-year PhD program.
Components
The scholarship comprises:
- a tuition fee offset/scholarship
- a living allowance stipend.
The value of each stipend scholarship is $39,700 per annum (full time, indexed) for three years.
How to apply
Before submitting your application, submit an expression of interest (EOI) to Professor Jia Wu at jia.wu@mq.edu.au.
Your EOI should include:
- a detailed CV, including a full educational background and a complete list of publications
- your official Master’s academic transcript
- your Master’s thesis report and evaluation result/grade
- your official English language test results. See English language admission requirements.