Our school aims to improve how we understand and take care of the world we live in through sustainable energy solutions and intelligent technology.
We focus on high-quality, cross-disciplinary teaching and research in robotic vision, machine learning, video analytics, wireless power transfer, microgrids, renewable energy integration and superconductivity.
Engage
Gain access to our expertise, find next-generation talent or join our thriving alumni community.
Our people
Our staff collaborate on projects that lead to real-world impact and help shape the future.
Our research
Our school is home to award-winning research facilities and research centres. These include the Da Vinci Precinct, a Brisbane Airport-based area where we engage in aerospace automation research and development, and our Microgrid Facility, a purpose-built environment for investigating practical and efficient grid integration of renewable energy solutions.
We also collaborate on interdisciplinary projects across QUT.
Centre for Robotics
We conduct world-leading research in intelligent robotics, translating fundamental research into real-world outcomes that benefit industry and society.
Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT)
We conduct world-class research, provide postgraduate research training and undertake commercial research, industrial consultancy and product development in artificial intelligence, machine learning, computer vision, and signal processing.
Power Engineering
We are a multidisciplinary research group with a broad skill set covering the areas of modern power systems, power electronics, electrical machines and superconducting technology.
Centre for Clean Energy Technologies and Practices
We address complex transport challenges and define the future of land transport mobility. We bring real technical, legal and social solutions for the constantly evolving mobility demand in our globalised and digitised society.
Visit the Centre for Clean Energy Technologies and Practices website
Future Farming
We bring together elements of technology, society, and biology and enable the use of information, extracted from purposefully collected data, to manage agricultural production systems: optimise yield and quality and increase efficiency whilst ensuring sustainability.
Robotic Vision Australia
Robotic Vision Australia is a national community of researchers and professionals, passionate about the potential for robotics, computer vision and AI to solve many of the world's grand challenges. Robotic Vision Australia has been established to lead an agenda around Australia's uptake of these innovative technologies and what we need to do to realise our potential as world leaders in this field.
Centre for Biomedical Technologies
Our research and development focuses on better patient treatments and quality of life into the future using regenerative approaches, robotics and artificial intelligence, and advanced manufacturing to expand surgical possibilities and reduce complications.
Centre for the Environment
We bring research, government, industry and community together to create real-world solutions to the most pressing environmental challenges. We aim to deliver ground-breaking fundamental and applied research that conserves and restores environmental systems and ensures the sustainability of natural resources in our natural, production and built landscapes.
Centre for Data Science
We draw together capability in data science from across Australia, providing a centralised hub for world-class data science research, unique training opportunities, and active external engagement.
Our facilities
Courses
Our students learn to design and maintain electrical systems and devices across a wide range of applications and industries.
News and events
Research finds many upsides for local governments that look to employ chatbots
QUT researchers have homed in on AI-powered chatbots in the local government sector to look at their benefits and risks, what they are used for and why, and how users view them.
Deep learning enables faster, more accurate decisions on shoulder abnormalities treatment
QUT scientists have developed a deep learning framework to detect shoulder abnormalities such as fractures in X-ray images with 99 per cent accuracy to enable clinicians to make correct and speedy decisions in emergency situations.
Realistic computer modelling of Queensland heritage masonry structures
Many heritage buildings in Queensland towns and cities have characteristic details that can make them susceptible to earthquakes, a survey of pre-1945 unreinforced masonry buildings by QUT civil engineers has found.
Contact us
Contact the School of Electrical Engineering and Robotics for more information on our courses, research and staff.