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Found 26 matching student topics

Displaying 1–12 of 26 results

Contributions and risks of smart urban technology-driven approaches to sustainable development

The prospects of smart urban technologies range from expanding infrastructure capacity to generating new services, from reducing emissions to engaging the public, from minimising human errors to improved decision-making, and from supporting sustainable development to improving performances of commercial enterprises and cities. The most popular technologies in the context of cities include, but are not limited to:internet-of-things (IoT)autonomous vehicles (AV)Mobility-as-a-Service (MaaS)bigdata5G/6Groboticsblockchaincloud computing3D printingaugmented and virtual reality (AR/VR)digital twinsartificial intelligence (AI).These disruptive technologies are critical in transforming our cities into smarter …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Architecture and Built Environment

The Challenge of Neural Interfaces to Law

Dr Scott Kiel-Chisholm is looking for PhD/MPhil candidates considering the legal dimensions from the development and adoption of neural interfaces. We are interested in looking for candidates looking at civil and criminal implications, comparative legal analysis and the legal and quasi-legal implications of neural interfaces for supra-legal institutions like the WTO and the EU. This topic is led by the QUT School of Law within the Datafication and Automation of Human Life research group.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Business and Law
School
School of Law

Technology, Innovation and Health

Professor Belinda Bennett is interested in talking to students who wish to undertake research on legal issues related to technology, innovation and health, regulation of innovative health technologies, legal issues related to genomics, the use of artificial intelligence in health care, and the use of robotics in health care.

Study level
PhD, Master of Philosophy
Faculty
Faculty of Business and Law
School
School of Law
Research centre(s)

Australian Centre for Health Law Research

Advanced artificial intelligence based ultrasound imaging applications

Our research in the space of advanced quantitative medical imaging is investigating how to use ultrasound as a real time volumetric mapping tool of human tissues, to guide in a reliable and accurate way complex medical procedures1. We have developed several novel methods which make use of the most cutting-edge artificial intelligence technology2. For example, to show where the treatment target and the organs at risk are at all times during treatments in radiation therapy3, 4; or to inform robots …

Study level
PhD, Master of Philosophy
Faculty
1043076
School
School of Clinical Sciences
Research centre(s)
Centre for Biomedical Technologies

Predicting failure in robotic vision

Computer vision models predict where objects are in an image, and what those objects are. In robotics, these vision models are used to allow robots to perceive their environment and choose safe and smart actions based on this perception.Computer vision models can fail silently when exposed to unexpected or difficult environments - e.g. changes in camera viewpoints, changes in lighting, or when seeing new objects that haven't been seen before. This raises concerns about the safety of using vision models …

Study level
Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Augmented reality (AR) applications for robotics

Augmented reality (AR), or mixed reality, has become a mature technology with many possible practical applications in manufacturing, retail, navigation and entertainment.We're interested in using AR to support human-robot interaction. In this project, you'll investigate how a human can use AR to better understand how a robot perceives the world and to understand the robot's intentions.

Study level
Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Human robotic interaction prototyping toolkit

Design relies on prototyping methods to help envisage future design concepts and elicit feedback from potential users. A key challenge the design of human-robot interaction (HRI) with collaborative robots is the current lack of prototyping tools, techniques, and materials. Without good prototyping tools, it is difficult to move beyond existing solutions and develop new ways of interacting with robots that make them more accessible and easier for people to use.This project will develop a robot collaboration prototyping toolkit that combines …

Study level
PhD
Faculty
Faculty of Creative Industries, Education and Social Justice
School
School of Design
Research centre(s)

Design Lab

Robotic intention visualisation

Complex manufacturing environments characterised by high value and high product mix manufacturing processes pose challenges to Human-Robot Collaboration (HRC). Allowing people to see what robots are ‘thinking’ will allow workers to efficiently collaborate with co-located robotic partners. A tighter integration of work routines requires improved approaches to support awareness in human-robotic co-working spaces. There is a need for solutions that also let people see what the robot is intending to do so that they can also efficiently adjust their actions …

Study level
PhD
Faculty
Faculty of Creative Industries, Education and Social Justice
School
School of Design
Research centre(s)

Design Lab

Kidnapped robot in a digital twin environment

Mobile robots are starting to be commonly used in manufacturing environments but they often get confused when they are switched on and off regarding where they are and what they should be doing. This is known as the 'kidnapped robot' problem, where the robot is to re-localise itself each time it is rebooted or a new task is presented.The advent of 'digital twins' has enabled a robot to build a map of its environment, which can aid in its localisation. …

Study level
Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Space robotics: Scene understanding for Lunar/Mars Rover

The QUT Centre for Robotics is working with the Australian Space Agency on the newly established Australian space program, in which robots will play a key role. There are multiple PhD projects available to work on different aspect of developing a new Lunar Rover (and later Mars Rover) and in particular its intelligence and autonomy. Future rovers will not only need to conduct exploration and science missions as famous rovers such as NASA's Curiosity or Perseverance are doing right now …

Study level
PhD
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

Deep learning for robotics in open-world conditions

To fully integrate deep learning into robotics, it's important that deep learning systems can reliably estimate the uncertainty in their predictions. This allows robots to treat a deep neural network like any other sensor and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements or to accumulate information over time.Deep learning systems typically return scores from their softmax layers that are proportional to the system’s confidence. They are not calibrated probabilities and …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

Deep learning for robotics in open-world conditions: Uncertainty, continuous learning, active learning

In order to fully integrate deep learning into robotics, it is important that deep learning systems can reliably estimate the uncertainty in their predictions. This would allow robots to treat a deep neural network like any other sensor, and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements or to accumulate information over time.Deep learning systems, e.g. for classification or detection, typically return scores from their softmax layers that are proportional to …

Study level
PhD
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

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