Two PhD scholarships (each equivalent to an APA scholarship) are available. The scholarship consists of a $31,260 (tax free) stipend per year for the duration of three years, plus a possible 6 month extension (depending on excellent progress).
Develop effective machine learning and data mining techniques for identifying fuel loss detection at service stations.
This project aims to develop effective techniques to identify the sources of fuel losses, such as leaks and calibration errors in underground storage tanks at service stations. Monitoring fuel losses at service stations is influenced by many external factors which can be difficult to predict. The project expects to use machine learning to develop the techniques and test them with live data at service stations. The expected outcomes are a set of tailor-made machine learning techniques for effective fuel loss detection and a software suite that can be easily incorporated into the normal operation of service stations. This should reduce the costs to the petroleum industry from wasteful leaks and the environmental damage caused by these leaks. The project aims to achieve the following specific objectives:
This is a project funded by an ARC Linkage Grant (LP190100991) over three years from 2020 to 2022.
Two PhD scholarships (each equivalent to an APA scholarship) are available. The scholarship consists of a $31,260 (tax free) stipend per year for the duration of three years, plus a possible 6 month extension (depending on excellent progress).
Two (2).
Candidates with backgrounds in machine learning and data mining are encouraged to apply.
To be eligible for this scholarship you must:
It will be highly desirable if you have a GPA of 3.5 or above, and solid experience in machine learning and data mining applications.
Candidates should contact Professor Xiaodong Li at xiaodong.li@rmit.edu.au. Prospective candidates should provide CV, academic transcripts and a written expression of interest before lodging any application with SGR.
Applications are open now.
Applications will close when the positions are filled.
We are particularly interested in applicants with solid experience in developing machine learning and data mining solutions to real-world problems.
This scholarship will be governed by RMIT University's Research Scholarship Terms and Conditions.
Professor Xiaodong Li at xiaodong.li@rmit.edu.au
Acknowledgement of country
RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.