Queensland invites applicants for PhD scholarships in unlocking urban airspace for drone transport

Queensland University of Technology invites applicants for PhD scholarships available for
(1) drone traffic management research; and (2) drone risk and reliability research.

The university seeks highly-motivated PhD candidates with Engineering, Math, Statistics or Computer Science backgrounds to be part of the project.

Successful applicants will conduct theoretical and applied research in data science, robotics and aerospace. They will use data and statistical analysis and visualisation, complex algorithm development, and simulation, to produce cutting-edge research results in drone (unmanned) traffic management for Australia.

As part of this project the candidate will:

  • Solve real problems for the fastest growing sector of aviation worldwide with potential commercialization opportunities.
  • Have direct interaction with the aviation industry and authorities, both locally and internationally
  • Gain 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

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.

The project is supervised by Dr Aaron McFadyen and academics from the School of Electrical Engineering and Robotics and School of Math. This scholarship is part of a Discovery Early Career Research Grant (DECRA) funded by the Australian Research Council (ARC) and supported by QUT’s Centre for Robotics and Centre for Data Science.

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