QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 488 matching student topics
Displaying 481–488 of 488 results
Physics-informed reinforcement learning for complex environments, using graph neural networks
Neglecting to incorporate physics information into world models for reinforcement learning leads to reduced adaptability to dynamic and complex environments and overall learning outcomes.In this project, we endeavour to develop and implement learnable models in reinforcement learning (RL) based on graph neural networks (GNNs). These models will integrate object and relation-centric representations to enable accurate predictions, strong generalization, and system identification in complex, dynamical systems. Additionally, we will focus on leveraging extensive world knowledge or physics information to refine representations …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Investigating community advocacy in response to aircraft noise pollution in Brisbane: an ethnographic study
The flight path design and community engagement practices associated with Brisbane Airport have long been criticised for prioritising profit over community wellbeing, leading to excessive aircraft noise pollution. These issues have now amounted to a federal Senate Inquiry and an investigation by the Commonwealth Ombudsman.This PhD research project aims to explore the dynamics between Brisbane Airport and the affected residential communities across more than 220 suburbs, drawing inspiration from a similar study conducted into the social engineering practices of Schiphol …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Design
- Research centre(s)
- Digital Media Research Centre
Design Lab
Empowering communities with DataCare: ethical data practices for smart cities
Smart cities hold immense potential for progress, but their success hinges on citizen empowerment and ethical data practices. Our research initiative, DataCare, aims at reshaping the landscape of smart cities by prioritising citizens, communities, and small businesses. This project, developed in collaboration with Brisbane Residents United (BRU), focuses on transforming smart cities from profit-driven entities to community-led developments.BRU is a community association serving as a vital grassroots advocacy and peer support network for suburban and local resident groups across Greater …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Design
- Research centre(s)
- Digital Media Research Centre
Design Lab
Immersive audio data visualisation for better engagement of residential communities exposed to aircraft noise pollution
This PhD project addresses the significant issue of misleading noise data in the context of residential communities exposed to aircraft noise pollution. Despite efforts by authorities to provide noise exposure forecasts and information based on the Australian Noise Exposure Forecast (ANEF) approach, many communities feel misled by the noise contours presented to them. Experiences from previous major development projects at Australian airports have shown a range of problems with relying solely on the ANEF as a noise information tool as …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Design
- Research centre(s)
-
Design Lab
Hospital readmission prediction with domain knowledge
The Australian Commission on Safety and Quality in Health Care has highlighted that reducing avoidable hospital readmissions supports better health outcomes, improves patient safety and leads to greater efficiency in the health system. Previous studies have reported that up to 11% of the emergency (ED) population are 'heavy users' with a higher prevalence of psychosocial problems and often co-existing chronic medical conditions. All Australian governments have committed to reforms under the National Health Reform Agreement Addendum,1 and the ability to …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Interpretable software vulnerability detection using deep learning techniques
Software vulnerabilities have been considered as significant reliability threats to the general public, especially critical infrastructures. Many approaches have been proposed to detect vulnerabilities in source code to avoid any damages they pose when exploited. Conventional approaches include static analysis and dynamic analysis. Static analysis uses pre-defined patterns or vulnerability dataset to scan and examine software source code to identify potential vulnerable code snippets. These patterns are manually crafted or identified by software developers or security experts, which are time-consuming. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Understanding user behaviour in virtual power plant (VPP) communities
Virtual power plants (VPP) provide a viable solution to integrate intermittent renewable energy sources into the grid, where a transition from centralized to decentralized energy distribution can provide economic and ecological benefits and facilitate citizen empowerment and a sense of community. However, consumers are reluctant to adopt distributed energy systems such as rooftop solar panels and household and community battery storage, which provide electricity generation and storage technologies that are located close to the point of use, as opposed to …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Quantum machine learning
Quantum machine learning is the integration of quantum algorithms within machine learning programs with great potential to solve complex problems. For instance, Google’s Sycamore processor (61) performs in 200 seconds a task that would require 10,000 years using a classical computer.
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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