A PhD scholarship in the field of machine condition monitoring, with focus on developing digital-twin based framework and physics-informed methodologies, is available at the Centre for Audio, Acoustics and Vibration, University of Technology Sydney.
Image by putilov_denis | Adobe Stock
About the Scholarship
TO APPLY
Send your CV and letter of interest to
Associate Professor JC Ji & Professor XiaoJun Chang
Jin.Ji@uts.edu.au and/or xiaojun.chang@uts.edu.au
Applications close : When filled.
The scholarship will be at a rate of AUD$32,500 p.a. for up to 3.5 years, working on a newly established ARC Linkage Project with research collaboration between the University of Technology Sydney (UTS), UNSW Sydney, and the mining industry.
The UTS Centre for Audio, Acoustics and Vibration (CAAV) and the Australian Artificial Intelligence Institute (AAII) is recruiting for a PhD student to work in the field of machine condition monitoring, with focus on developing digital-twin based framework and physics-informed methodologies for prognostics. Based at UTS, the student will work with an interdisciplinary team from UNSW and UTS, to focus on areas from mechanical and civil engineering to computer science. This research work is supported by an ARC LP project on Intelligent condition monitoring system for mining vibrating equipment. Digital twin technology is a relatively new framework with a wide range of applications in all industry fields.
The prospective PhD student will have the opportunity to work with renowned researchers from UNSW and UTS, as well as the industry partner, in two or more of the following areas of crack initiation and fracture mechanics, machine condition monitoring, machine dynamics, deep learning and artificial intelligence, mining equipment, and applications of novel digital-twin framework to mining industry (and beyond).
Another similar scholarship is available for the student to do a PhD at UNSW, working collaboratively on the ARC LP project.
Brief Introduction to the project
This project aims to develop an intelligent condition-monitoring system for screening machines which are widely used for classifying mineral particles in the mining industry. This project will develop new vibration-based methodologies and techniques for fault diagnostics and remaining useful life prediction of bearings and gears in situations with multiple complex sources and interferences. The monitoring system, as the expected outcomes of this project, will modernise the current maintenance practices towards condition-based predictive maintenance, reducing unplanned downtime, increasing productivity and reducing maintenance costs for the Australian mining industry. It will also add more value to the Australian manufactured products.
For more information on the project: https://dataportal.arc.gov.au/NCGP/Web/Grant/Grants#/20/1//LP220100389/
Application Requirements
- University Degree (Bachelor, 1st class honours or Bachelor and Masters) in Mechanical/Mechantronic/Aerospace engineering/Computer Science, or related disciplines with a strong academic record and;
- Demonstrated experience in undertaking research in the fields of vibration, dynamic modelling, mechanical design, deep learning, and artificial intelligence.
- Open to Domestic students (Australian citizen and Australian permanent residents) only.
How To Apply
Applicants should send their CV and letter of interest addressing the application requirements as well as their academic transcripts to jin.ji@uts.edu.au and/or to xiaojun.chang@uts.edu.au. Applications close when the positions are filled. More details on research team will be provided upon request.