CQUniversity and Freelance Robotics National Industry PhD Program Scholarship

Status

Closed

Scholarship Value

$AU48,894 Stipend per year (indexed)

Length of Scholarship

3 years

Number Available

1

Funding Type

Research

The project's goal is to incorporate guidance automation and expanded data analytics into the current Orion Orchard imager and harvester technologies, which currently rely on human guidance. The project aims to enhance these existing technologies to create a solution suitable for mango orchards. Key aspects include the deployment of an autonomous ground vehicle (UGV) designed to transport both the orchard imager and auto-harvester while navigating the orchard by intelligently seeking the optimal path. The UGV will be capable of operating in GPS-denied environments, enabling automated path planning, multi-vehicle coordination, and centimeter-level precision for autonomous picking activities. The scholarship will facilitate a collaboration with Freelance Robotics, involving time spent in both industry settings (Redland Bay QLD 4165 and associated field research) and at the CQU Brisbane campus.
  1. An undergraduate with Honors or postgraduate degree in Computer Science, Electrical Engineering, Robotics, Mathematics, Mechatronics, or related fields. 
  2. Open Drivers License

The ideal candidate would possess: 

  1. an undergraduate with Honours or postgraduate degree in Computer Science, Electrical Engineering, Robotics, Mathematics, Mechatronics, or related areas.
  2. full-stack website development capabilities using Python, Django, and Vue.js/React. 
  3. experience in implementing multiple levels of user management, including defining roles, authentication, and permissions. 
  4. proficiency in C++, Python, and MATLAB programming languages. 
  5. experience with real-time systems development.
  6. knowledge of robotics principles such as kinematics and dynamics. 
  7. experience in both linear and non-linear control systems.
  8. experience with AI and ML algorithms, particularly in computer vision and sensor fusion using frameworks like TensorFlow, PyTorch, or OpenCV. 
  9. experience in computer vision techniques for object detection, tracking, and classification. 
  10. understanding of sensor technologies such as LiDAR, radar, and cameras. 
  11. knowledge of SLAM techniques. 
  12. experience with GPS, IMU, and other localization technologies. 
  13. experience in autonomous vehicle or related robotics projects. 
  14. ability to collaborate effectively in multidisciplinary teams comprising hardware engineers, software developers, and researchers. 
  15. excellent verbal and written communication skills to communicate technical concepts to technical, e.g., as demonstrated in past published work, and non-technical stakeholders. 
  16. a strong ability to adapt quickly to new technologies and methodologies. 
  17. demonstrated capability to implement technology in a field environment. 
  18. preferably work or study rights in Australia. 

Candidates should address all selection criteria but are not expected to be fluent in all areas.

Please submit your applications or enquiries to:

Dr Zhenglin Wang at z.wang@cqu.edu.au 

All applicants will be notified of the outcome via email. 

Please submit a CV with the following structure: name, Linked In or other profiles, H-index if applicable, citizenship and Australia status, home address, current address, aspirational statement of 100-200 words (detailing your professional interests and goals), educational history, work history, publications, core skills, 1-2 paragraphs detailing a delivered technology project. Please also submit a cover letter addressing each of the 18 selection criteria in order.