What you'll receive
31 January 2024
You'll receive a stipend scholarship of $30,000 per annum for a maximum
duration of 3.5 years while undertaking a QUT PhD. The duration includes an
extension of up to 6 months (PhD) if approved for your candidature. This is
the full-time, tax-free rate which will index annually. The PhD project is
linked with industry partner Seeing Machines Pty Ltd, so you may be eligible
to apply for a stipend top-up via the National Industry PhD Program.
If you're an international student, you will also receive a tuition fee
sponsorship for your research degree.
As the scholarship recipient, you will have the opportunity to work with a
team of leading researchers, to undertake your own innovative research in and
across the field.
To apply for this scholarship, you must meet the entry requirements for a QUT
Doctor of Philosophy, including any English language requirements.
The essential criteria for the scholarship include:
enrolling as a full-time, internal student
have a Master’s degree (or equivalent) in one of these areas:
Other related discipline
How to apply
Apply for this scholarship at the same time you apply for admission to a QUT
research degree / Doctor of Philosophy.
The first step is to email Professor Ronald Schroeter detailing your academic
and research background, your motivation to research in this field and
interest in this scholarship, and include your CV.
If supported to apply, you will then submit an Expression of Interest (EOI)
following the advice at How to apply for a research degree. In your EOI,
nominate Professor Ronald Schroeter as your proposed principal supervisor, and
copy the link to this scholarship website into question 2 of the financial
About the scholarship
We are seeking a highly motivated and talented Vision Science and Human
Factors PhD Researcher to join an interdisciplinary research project,
conducted in collaboration with Queensland University of Technology (QUT)
and industry partner Seeing Machines—world leader in human-machine interaction
and an industry leader in artificial intelligence (AI), that enable machines
to see, understand and assist the people who are using them.
The project aims to advance the understanding of human behaviour during
automated driving, with a strong focus on eye, gaze, and head tracking using
Seeing Machines’ optimised driver monitoring system technology. The new
knowledge is expected to inform novel strategies that contribute to the safe
introduction of Automated Vehicles.
Conduct comprehensive literature reviews to understand the current state
of HCI/HMI research, focusing on driver engagement and attention sharing
in autonomous driving contexts.
Design and develop novel techniques and interfaces for monitoring and
assessing driver engagement in L2-L4 driving environments.
Collaborate with interdisciplinary research teams from QUT and Seeing
Machines to integrate findings from other PhD projects into the broader
Work closely with industry partner Seeing Machines to gain insights into
driver monitoring systems and leverage their expertise in interface
Conduct empirical studies to evaluate the effectiveness of different HMI
designs in sharing attention between drivers and autonomous systems.
Analyze qualitative and quantitative data obtained from user studies to
gain insights into user behavior, preferences, and challenges.
Contribute to the development of guidelines and best practices for
designing intuitive and effective HMIs in autonomous vehicles.
Collaborate with other PhD researchers to support interdisciplinary
learning and foster a cohesive research environment.
Publish research findings in high-impact journals and present at relevant
conferences and workshops.
Contribute to the preparation of research reports, project documentation,
and funding proposals.
Engage in regular progress reporting and participate in supervision and
Qualifications and Skills
A Master's degree (or equivalent) in Human-Computer Interaction, Human
Factors, Cognitive Psychology, or a related discipline.
Strong research background in HCI/HMI, interface design, cognitive
psychology, or a relevant field.
Familiarity with research methods in HCI, including user-centered design,
usability testing, and user experience evaluation.
Proficiency in design and prototyping tools such as Adobe XD, Sketch, or
Experience in conducting empirical studies involving user participants,
preferably in the field of human-automation interaction.
Knowledge of driver monitoring systems and their applications in automated
driving is desirable.
Strong analytical and problem-solving skills, with the ability to
translate user insights into actionable design recommendations.
Excellent communication skills, both written and verbal, for presenting
research findings and collaborating with interdisciplinary teams.
Ability to work independently and as part of a team, managing multiple
tasks and priorities effectively.
Demonstrated publication record (or potential) in peer-reviewed
conferences or journals is advantageous.