• University: University of Sydney
  • Stipend: $32,192- $50,291 and fee off-set, with an additional $5,500 top-up from Defence Innovation Network
  • Requirement: Must be Australian citizens or permanent residents and enrol full-time
  • Start Date: ASAP
  • Contact: A/Prof Nicholas Lawson, nicholas.lawson@sydney.edu.au


Drones of all sizes have significantly changed the way we monitor, transport, and do research. Their applications continue to grow at a phenomenal rate, and we are also now considering new passenger transport systems based on advanced air mobility (AAM).

With any drone design, there are many systems ranging from propulsion to navigation and power systems. Their reliability is one area of concern and data on failure modes of some systems is critical to drone future deployment.

Within the School of Aerospace, Mechanical and Mechatronic Engineering, we have constructed a preliminary test bed that allows us to test and monitor drone power and propulsion systems.

The basic test bed was commissioned in a recent DIN Pilot Project, and we now have funding for a PhD scholarship to extend this work. Using and modifying the current rig, we would like to record data from generic drone electric power and propulsion systems and use the data to predict system failures using artificial intelligence (AI) and machine learning (ML) algorithms.

We are looking for a highly motivated aerospace, mechanical, or mechanics student who is interested in running and modifying our rig and studying failure recognition methods in a 3-year fully funded PhD program.

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