Decentralised Collaborative Predictive Analytics on Personal Smart Devices

Project opportunity

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

This project tackles the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data. The knowledge to be generated concerns privacy, fairness, and resource efficiency in the era of Internet of Things. The expected outcomes include a collaborative learning paradigm for building personalised models on personal smart devices in open and fully decentralised settings. Privacy and model fairness are core tenets of the paradigm. Personalised predictive analytics is frontier research that will position Australia at the forefront of AI and give business the tools needed to deploy innovative business systems for market exploitation with a secure, equitable and competitive advantage.

Scholarship value

As a scholarship recipient, you'll receive: 

  • living stipend of $32,192 per annum tax free (2023 rate), indexed annually
  • tuition fees covered
  • single Overseas Student Health Cover (OSHC)


Associate Professor Hongzhi Yin

School of Information Technology and Electrical Engineering


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 programming with various machine learning packages, experiment design and academic writing would be of benefit to someone working on this project. Collaborating with leading experts to deliver demonstrable research outcomes, presenting data-driven insights to both technical and non-technical audiences via research publications, technical reports, and oral presentations is also beneficial.

The applicant will demonstrate academic achievement in the field(s) of computer science or data science and the potential for scholastic success.

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

Latest commencement date

If you are the successful candidate, you must commence by Research Quarter 3, 2023. You should apply at least 3 months prior to the research quarter commencement date.

If you are an international applicant, you may need to apply much earlier for visa requirements.

How to apply

You apply for this project as part of your PhD program application.

View application process