Data-Driven Optimisation for Next-Generation Digital Manufacturing Systems

This research will involve:

• Developing advanced data analytics methodologies to support real-time monitoring and control in digital manufacturing processes.
• Leveraging large-scale production data to model, analyse, and optimise key operational parameters, improving consistency, precision, and cost-efficiency across workflows.

By enabling data-informed decision-making throughout the manufacturing lifecycle, this project aims to enhance production reliability, reduce downtime, and improve overall resource efficiency. The outcomes have strong potential for commercial application, supporting more sustainable, adaptive, and economically viable manufacturing systems.

This project is part of the prestigious ARC Industrial Transformation Research Hub for Future Digital Manufacturing, a national collaboration between seven leading Australian universities and ten industry partners. Together, the Hub is driving innovation to tackle real-world manufacturing challenges and strengthen Australia's global competitiveness.

As a PhD researcher, you will work with cutting-edge facilities at UQ’s School of Mechanical and Mining Engineering (SoMME) and School of Electrical Engineering and Computer Science (SEECS), both known for their world-class research in advanced manufacturing and computational technologies. You’ll collaborate with experienced, supportive research teams and benefit from a vibrant academic community through regular seminars, research showcases, and events like the AMPAM seminar series and the annual EAIT Postgraduate Conference, providing valuable opportunities to share your work and build your professional network.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

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 PyTorch, ROS and data analytics tools would be of benefit to someone working on this project.

A background or knowledge of visual analytics is highly desirable.

You must submit an expression of interest (EOI) by 31 December, 2025 31 December, 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 Professor Helen Huang (huang@itee.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: OPTIMISATION-HUANG
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/data-driven-optimisation-next-generation-digital-manufacturing-systems

Submit an EOI