FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW

Machine learning to support the civil construction industry to create a safer future for employees

   Faculty of IT

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr P Zhou  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

A fully funded PhD scholarship is available with the Human-in-the-Loop Analytics (HiLA) program at Monash University, Melbourne, Australia.

HiLA is a Graduate Research Industry Partnership (GRIP) that has been established to allow Monash University and its Industry partners to collaborate on common research objectives.
With the support of Monash’ partners, HiLA PhD scholarships are provided with significant benefits beyond other scholarships on offer. These benefits include:

A fully funded PhD scholarship with Monash University that is available for domestic (Australian) and international students. The 3 to 3.5 year award covers all course fees and a $30,000 AUD per year tax-free stipend;
An internship with the Industry partner - where PhD candidates will spend a portion of their candidature located on site and being supported by the partner;
Access to real world problems supported by real world data;
Where the industry partner is located overseas or interstate, travel and accommodation (when working on site with the partner);
Travel and incidental support for conferences;
Enrollment in the HiLA professional development program;

All HiLA PhD scholarships will commence in Semester 2, 2019.

The successful candidate will be supervised by Dr Paul Zhou from the Faculty of Business and Economics.

The specific project will support 2 X PhD candidates and centers around the following research opportunity:

The highest priority on any work site especially, a civil construction site, is safety.
The Australian Work Health and Safety Strategy 2012-2022 describes the construction industry more broadly, as a priority industry for work health and safety. While many process exist that govern safe operations in a construction workplace any opportunity to further reduce the risk to workers is of immense value to society. This research will consider novel ways of classifying individual worker daily and intermediary safety checks, diary, project and company safety related data and operational scheduling data and discover insights that can help better predict safety risks to workers on construction sites whilst improving productivity gains.

As a Graduate Research Industry Partnership project, the candidate will have access to knowledge and resources from Monash University and industry partner, Platformers who work with some of the biggest names in civil construction. The applicant will also have access to large scale proprietary datasets. It also provides the opportunity for research to be tested and implemented in solving real-world problems during the project cycle.

Candidates must fill out the online to Request to Apply form, which can be found at:

Please make sure you indicate that the PhD Topic is “Machine learning to support the civil construction industry to create a safer future for employees”;

In addition to filling out the form, a copy of your academic transcripts and CV should be emailed to [Email Address Removed].

Applicants must possess a Bachelor’s or equivalent degree with first-class Honours, and/or a distinction in a research Masters degree with relevant experience (e.g., data analysis, artificial intelligence, social informatics, psychology, human-computer interaction or data visualisation). Review of applications will begin immediately and short-listed candidates will be contacted for more information and invited to interview. The successful candidate will be invited to apply to Monash with the deadline for applications being the 30th June 2019.

International students that can demonstrate English proficiency are encouraged to apply.
PhD saved successfully
View saved PhDs