School of
Information Technology and Electrical Engineering




Data 61 offers scholarships to eligible PhD candidates working in data-related disciplines such as Analytics, Cyber-Physical Systems, Software and Computational Systems and Decision Sciences, through the Data61 CSIRO Scholarship Program.

Successful scholarship applicants will be enrolled at the University of Queensland, co-supervised by UQ academics and Data61 researchers, to work on real-world projects that deliver significant research outcomes for the national benefit.


Applicants may be Australian or international students. Applicants must meet UQ’s entry requirements for admission to the research higher degree program and must apply to both the University of Queensland and Data61 to be considered for a Data61 scholarship.

Duration and level of Award

Data61 offers full stipends, set at a rate equivalent to the Research Training Program (RTP) rate (indexed annually), and top-up scholarships of up to $10,000 pa, for periods up to 3.5 years.

How to apply

The School of ITEE offers the following research projects as opportunities for collaboration between UQ and Data61.

  • If you are interested in any of these projects you will first need to make contact with the UQ supervisor listed below.
  • If the supervisor agrees to supervise you and your qualifications and background are suitable for the project, you will then need to go through the application process for HDR study at UQ and meet entry requirements for admission. You will need to indicate in the online application form that you intend to apply for the Data61 scholarship program.
  • Once you have submitted your application with UQ, you can then contact the Data61 supervisor to obtain their written agreement to supervise the project in collaboration with the UQ supervisor. Please see information about how to apply for the Data61 scholarships.


Further enquiries should be directed to Data61 student scholarship administration at, and the HDR student administration office at the School of ITEE, UQ:

Available Projects:

Self-Supervised Learning for 3D Multimodal Perception

Potential impact of deep learning is limited due to the lack of large, annotated, and high-quality datasets in domains of interest. Annotating such datasets is laborious, costly and time-consuming. This project proposes to develop self-supervised learning systems to extract and use the relevant context given by strong prior spatio-temporal models (e.g. dense 3D reconstructions) as supervisory signals in training. This new concept will investigate model structures that encodes spatio-temporal data, and show rapid adaptation of models to new domains (few-shot learning) using trained embeddings layers (self-supervised, or prior data). 




Simultaneous Localization and Mapping (SLAM) is a key enabling component of driverless vehicles, robotics and augmented reality. The SLAM goal is to estimate pose of the vehicle and simultaneously generate dense 3D scene reconstruction. At CSIRO we have developed and deployed state-of-the-art 3D LiDAR-based SLAM systems for the past decade. There is a new direction of research at the intersection of deep learning and geometry-based 3D SLAM. The research in this PhD programme will develop algorithms for geometry-based Deep Learning SLAM in a dynamic and unstructured environment. The PhD programme will involve the development of self or semi-supervised learning methods to address the significant weakness of most current deep networks. 



Hyperspectral Deep Learning

Hyperspectral cameras are currently undergoing a change from bulky and expensive equipment towards mobile and portable devices. A hyperspectral camera comprises of hundreds of bands with shortwave dependencies. Compared to conventional colour cameras (RGB bands), one could use these shortwave dependencies to design and develop a deep network for object classification, semantic segmentation and scene understanding. Both spectral and spatial relationship needs to be modelled by the deep networks simultaneously. The research in this PhD programme will develop algorithms for hyperspectral deep learning. The PhD programme will involve the development of learning with self-supervision algorithms to address the significant weakness of most current deep networks.



Dynamic control and motion planning for climbing robots

One of the key challenges in many applications are access and inspections of vertical surfaces. The vertical surfaces could either be in natural domains like tree trunk and branches, or in industrial environments like support trusses, structural walls. The successful candidate will work with a team of CSIRO researchers to develop novel algorithms and implement them on CSIRO legged platforms.  

More information is available at: 



Long Term Localisation and Navigation for Autonomous Vehicles

The topic will focus on long term localisation using a combination of vision, laser and other sensors through intelligent sensor fusion. The goal is to perform autonomous navigation on a two-person medium-size electric vehicle that has been automated by the CSIRO. The successful candidate will have access to CSIRO's infrastructure and support from hardware and software engineers during the project.

More information:



Evolving body parts for robots

This project is designed to augment our collection of legged robots with specialised legs/end effectors, that are automatically designed through evolution and can be easily attached to our current chassis by “clipping on”. The legs will be designed using an Evolutionary Algorithm, based on their performance on several simulated environments. Simulation results will be validated by 3D printing the best designs and assessing on our physical test rig. We would like to use real-world information from the physical tests to enhance the accuracy of the simulator.

More information:



Whole body control for next generation of legged robots navigating in unstructured terrain

Legged robots such as six legged hexapods have a lot of potential in remote exploration as well as search and rescue tasks in difficult terrain.  This project aims to develop the next generation of legged robots capable of such tasks in real world scenarios with a focus on whole body control.  The successful candidate will get access to CSIRO’s state of the art robotics lab and work with a team of CSIRO researchers to implement developed control methods on real robots.

More information:



* Please note that while the student’s enrolment is administered by UQ, the scholarship is administered by Data61, so applicants are advised to check the Data61 website for further details and application deadlines, which may vary from University dates.