Associate Professor Jason Ford

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Doctor of Philosophy (Australian National University), Bachelor of Engineering (Australian National University), Bachelor of Science (Australian National University)

Jason is an Associate Professor in Electrical Engineering (Autonomous Systems) at the Queensland University of Technology (QUT). He graduated from the Australian National University with the B.Sc. and B.E. degrees from the Australian National University in 1995, and graduated with a PhD degree from the Australian National University in 1998. In 1998 Jason joined the Australian Defence Science and Technology Organisation (now called DSTG) as a research scientist (promoted to senior research scientist in 2000). In 2004 Jason was appointed a research fellow at the University of New South Wales at the Australian Defence Force Academy.  In 2005 Jason joined the Queensland University of Technology as a Research Fellow, before appointment as Lecturer in Electrical Engineering at the Queensland University of Technology in 2007 (promoted to Senior Lecturer in 2010 and Associate Professor in 2016).

Jason has invented, developed and commercialised aircraft flight control and related systems for general aviation aircraft; his technology is in aircraft operating in Australian, European and US airspaces.

Jason’s expertise is the area of autonomous aerial vehicles with a focus on: model based filtering, estimation, system identification, detection and control; and achieving industry impact. Current research interests include platform autonomy for infrastructure inspection and low signal-to-noise ratio anomalous signal detection with application in aerospace and other domains.

Research and industry impact highlights include:

  • Development and commercialisation of aircraft automation systems for infrastructure inspection within the ROAMES asset management system. This rare example of an Australian developed aircraft flight technology was Australian Research Council’s 2018 Engagement and Impact rated as High.
  • More than a decade of sustained research activity on the extremely challenging problem of replicating the human pilot vision system to create a vision based sense and avoid technology for autonomous aerial systems. A summary can be found in a feature article in IEEE Aerospace and Electronic System Magazine.




Additional information

Automated vision-based aircraft collision warning technologies
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
Start year
Collision Warning; Aerial robotics; National Airspace