Fully-funded PhD Position on Representing Physical Assets for Anomaly Detection

Fully-funded Ph.D. Scholarship Opportunity Available! 

We are seeking a Ph.D. student interested in working at the cutting edge of computer vision, deep learning and robotics. The position is based at the QUT Centre for Robotics as part of the new Australian Robotic Inspection and Asset Management Research Hub (ARIAM). As a member of ARIAM, you will work closely with industry partner Abyss Solutions to develop research that impacts real-world robotics problems. This position includes a $40,000 per year tax-free scholarship for 3.5 years (with no additional study fees). 

Project Overview 

At the ARIAM Hub, we envision a future where robots can reliably and safely inspect and manage Australia’s industrial assets, whether it be in the air, on the land, or underwater. For this to be possible, a robot must be able to check specific asset conditions, while also looking for any general irregularities or unusual conditions. Checking for general irregularities, known as anomaly detection, is a challenge for existing technology – it requires an understanding between expected changes to appearance, e.g. lighting, weather, collection of dirt, versus unexpected changes, e.g. damage, corrosion, etc.  

The goal of this project is to address this challenge – we want to use data gathered by mobile robotic platforms to build a representation of an environment that can be used to track changes in the state of the environment over time, both normal and anomalous. Using machine learning, this representation should be built with existing data, and should continuously update when new data is collected.  This project will pay special attention to dealing with large and extended assets at scale, at very high spatial and textural resolutions. 

About Us 

You will work in a collaborative, distributed research environment that spans across the QUT Centre for Robotics, the ARIAM Hub, and with industry partner Abyss. 

QUT Centre for Robotics (QCR) 

QCR conducts at-scale world-leading research in intelligent robotics; translates fundamental research into commercial and societal outcomes; is a leader in education, training and development of talent to meet growing demands for expertise in robotics and autonomous systems; and provides leadership in technological policy development and societal debate. With a large modern lab space, QCR comprises over 100 researchers and engineers who have access to a collection of equipment to support research, including many mobile robot platforms and robotic arms. QCR supports a flexible working environment. We support a diverse and inclusive atmosphere and encourage applications from women, Aboriginal Australians and Torres Strait Islander people. 

ARIAM Research Hub 

ARIAM is a new research group formed as a collaboration between leading academic researchers and industry experts from the Australian Robotics ecosystem. Supported by the Australian Research Council, ARIAM aims to deliver research excellence in robotics for asset management. As part of ARIAM, you will be a part of a wider network of academic and industry collaborators that spans 3 leading Australian universities: The University of Sydney, Queensland University of Technology and The Australian National University. This offers a unique opportunity to work closely with leading researchers distributed across Australia. 

Abyss Solutions 

The mission of Abyss Solutions is to make autonomous inspections accessible at scale. To achieve this mission, Abyss Solutions has positioned itself as a robotics company that combines the latest innovations. Our state-of-the-art AI systems provide actionable data analytics to create safer, easier and more comprehensive inspections which enable correct asset management decisions across multiple domains including, underwater, water surface, ground, in-air and space. Abyss Solutions has developed technology to inspect assets such as dams, reservoirs, canals, bridges, ship hulls and oil and gas platforms, and currently has more than 150 employees supporting these inspections through tech development, in-field operations, logistics and planning. You may be invited to spend some time on-site in the Abyss offices in Sydney. 

About You 

  • A bachelor’s degree in a relevant discipline 
  • Excellent communication skills (verbal and written) 
  • An interest in seeing your research have a direct impact in industrial applications 
  • Programming in C++ or Python  
  • Prior experience in machine learning, computer vision and/or image processing is desirable 

Apply Now 

Please email Dr Dimity Miller at d24.miller@qut.edu.au if you have any questions about the position. 

To apply, please email d24.miller@qut.edu.au, with the subject line “ARIAM Ph.D. Application:” and your name. Include the following: 

  • CV 
  • Unofficial transcripts 
  • 1 paragraph on why you’re excited for this position