Scholarship details

Study levels

Research

Student type

Future students

Study area

Science, technology and engineering and mathematics

Eligibility criteria

Academic performance

What you'll receive

The successful applicant will receive a scholarship, tax exempt and indexed annually of $28,597 per annum for a period of three years, with a possible 6 month extension, subject to satisfactory progress.

International students will also receive either:

  • an Australian Government Research Training Program (RTP) fees offset (International) or
  • a QUT research degree (HDR) tuition fee scholarship.

The Scholarship will be governed by QUTPRA Scholarship rules.

Additional funding to support fieldwork and research dissemination may also be available.

Eligibility

PhD scholarships are available for full-time, highly motivated PhD candidates to perform cutting-edge research in drone (unmanned) traffic management for Australia. The scholarships are part of a Discovery Early Career Research Grant (DECRA) funded by the Australian Research Council (ARC) and supported by QUT’s Centre for Robotics  (QCR, https://research.qut.edu.au/qcr/) and Centre for Data Science (CDS, https://www.qut.edu.au/research/centre-for-data-science).

Scholarships are open to domestic and international students residing in Australia with Engineering, Math, Statistics or Computer Science backgrounds.

To apply for this scholarship, you must:

  • Meet eligibility for admission to the PhD program at QUT (see https://www.qut.edu.au/research/study-with-us/phd) including any English language requirements for international students.
  • Hold a first- or second-class honors division 1 undergraduate degree in Math or Engineering (aerospace/avionics/aeronautical, robotics/mechatronics, electrical), or a master’s degree with a significant research component (or equivalent).
  • Excellent communication skills (written and verbal) in English.
  • Programming experience

Desirable skills include:

  • Data analytics (inc. statistics, machine learning, database management and data visualization)
  • Simulation and modelling
  • Decision and control (inc. optimization)
  • Aviation industry experience (inc. drone operation, aviation authorities)

How to apply

Interested applicants should contact Dr Aaron McFadyen prior to submitting an Expression of Interest (EOI).  Applicants may then be requested to submit an EOI as per QUT's how to apply website.

The EOI must include:

  • Up-to-date curriculum vitae (CV), academic transcripts and recent IELTS or equivalent score for international applicants
  • One (1) page research proposal addressing the scholarship topic. Include references to all relevant publications, thesis or reports authored by applicant addressing the scholarship topic.
  • Two (2) references (inc. at least 1 academic reference)

The EOI must indicate that you are applying for this scholarship and must have Dr Aaron McFadyen nominated as your potential supervisor.

What happens next?

Application assessments will commence from February 1st, 2021 and the scholarship will remain open until suitable candidates are found. Applicants will be contacted directly with an outcome of their Expression of Interest (EOI).

For questions about the research project please contact Dr Aaron McFadyen at aaron.mcfadyen@qut.edu.au

For questions about the application process, please contact hdr@qut.edu.au

About the scholarship

Background:

Air transportation systems have the potential to be revolutionized worldwide by drone technologies (unmanned aircraft) that could bring huge commercial (trillion dollar), economic and social (improved security, emergency and medical services, package delivery etc.) benefits. To unlock these benefits, drones need fast, regular and safe access to urban airspace. Currently, access is restricted due to mid-air collision risk concerns and the absence of unmanned traffic management (UTM) systems to manage these risks.

New data driven modelling and simulation techniques are crucial to enhancing our understanding of the collision risk associated with drone operations, and provide the key enabling capability to underpin drone traffic management systems. This research will derive such models and investigate their use in real automated drone traffic management applications. Example applications include low-altitude airspace design, automated flight approval, routing and scheduling through to separation standard development and counter-drone surveillance.

Overview:

PhD projects will be conducted within the wider context of the funded DECRA project, providing successful applicants the opportunity to generate new knowledge and create novel technologies for drone traffic management. Projects will typically require the use of programming for activities such as data/statistical analysis and visualisation, complex algorithm development, and simulation. Specific details of the PhD topic and activities will be negotiated with successful applicants with scope to conduct theoretical and applied research in data science, robotics and aerospace.

Expectations:

This project is driven by achieving practical outcomes that can used to support real drone operations. It is expected that the new knowledge generated will contribute to high-quality publications and technical briefs as well as inform industry stakeholders and policy makers where appropriate.

Project Benefits:

  • Solving real problems for the fastest growing sector of aviation worldwide with potential commercialization opportunities.
  • Direct interaction with the aviation industry and authorities, both locally and internationally
  • Access to large unique datasets and high-performance computing equipment
  • Develop multidisciplinary and transferable skills across math, engineering and aviation by working within a team of national and international experts located in Australia.
  • Travel to national and international conferences

Supervision and Support:

PhD projects will be under the supervision of Dr Aaron McFadyen and academics from the School of Electrical Engineering and Robotics and School of Math. Support may be provided from external project collaborators, QUT’s Centre for Robotics (QCR) and Centre for Data Science (CDS), giving successful applicants access to multiple domain experts and peers.

Location:

Science and Engineering Faculty at Queensland University of Technology, Gardens Point, Brisbane.

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