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 16 matching student topics
Displaying 1–12 of 16 results
Hierarchical visualisation of large social networks
Networks have been extensively used to capture social interactions, by representing individuals as nodes and their relationships as edges.Such networks have been used to model the spread of epidemics. A few nodes are 'infected', and over time they gradually infect their neighbours on the network, who in turn infect their neighbours, etc. This type of model can then be used to simulate different intervention strategies aimed at containing outbreaks.However, an important limitation is the difficulty to visualise these networks when …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Trust in Internet-of-Things with blockchain
Blockchain is an unchangeable, distributed database that provides trust in data once it is stored on the database. However, in Internet-of-Things (IoT), the data is an observation of physical context and is susceptible to noise, drift, or malicious alterations. Sensors may even be decoupled from their intended context by an attacker, which may compromise the blockchain data and its value for guiding decisions.This project aims to develop an innovative approach for pervasive trust in IoT, underpinned by blockchain. The research …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
Understanding urban complexity for climate resilience
Addressing the urgent need for community resilience amid escalating climate risks, including floods, extreme heat, and bushfires, is crucial for burgeoning cities. These cities comprise intricate networks of social, ecological, physical, and technological subsystems with structural and functional interdependencies. Understanding this complexity is vital for evaluating a city's resilience to climate risks and formulating effective policies and planning strategies. By applying complexity science principles, researchers can illuminate the dynamic relationships within these networks, revealing opportunities for sustainable urban development and …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
- Research centre(s)
-
Centre for the Environment
Representation learning for anti-microbial resistance
This project is about using neural network models help us understand Anti-Microbial Resistance (AMR), a phenomenon in which bacteria adapt to reduce the effectiveness of antibiotics, usually through a process known as Lateral or Horizontal Gene Transfer - where genes are included in the organism from other sources.Our focus will be on learning compact vector representations of biological sequences known to be associated with AMR genes. By encoding DNA sequences in this way we can more rapidly identify AMR genes …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Board interlocks and firm decisions
Board interlocks research is one of the most vibrant areas in corporate governance research. A board interlock is a tie created by two firms sharing a common director. In other words, a director can hold multiple directorships in more than one firm. Board interlocks reflect complex inter-organisational relationships which play an important role in determining a firm’s strategies and structures.Prior research finds that board interlocks have an impact on reducing environmental uncertainty, gaining access to diverse and unique information, diffusing …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Business and Law
- School
- School of Accountancy
Optimising bone shape with memory networks
Bone is a dynamic tissue that optimises its shape to the mechanical loads that it carries. Bone mass is accrued where loads are high, and reduced where loads are low. This adaptation of bone tissue to mechanical loads is well-known and observed in many instances. However, what serves as a reference mechanical state in this shape optimisation remains largely unknown.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
Road map to local circular communities: strategies, barriers, enablers.
The textile industry is one of the world’s largest, with global sales in 2016 of USD 1.5 trillion. It is also one of the most polluting industries, producing 20% of global wastewater, and contributing to 10% of carbon emissions. Fashion generates large amounts of waste, and has negative social and health impacts for workers.Circular economy would address these issues, keep clothes and textiles at their highest value and keep clothes in continuous circulation. The transformation of the sector requires a …
- Study level
- PhD
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Design
- Research centre(s)
-
Design Lab
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
Network Flow Improvement
Network flow is impeded by the arcs present in the network and their associated length/weighting. Arcs can be added or removed to debottleneck the network. But which ones? At what cost?
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
Evaluating the challenge of ‘fake news’ and other malinformation
Encompassed by the disputed term ‘fake news’, overtly or covertly biased, skewed, or falsified reports claiming to present factual information present a critical challenge to the effective dissemination of news and information across society.This ARC Discovery project in the QUT Digital Media Research Centre conducts a systematic, large-scale, mixed-methods analysis of empirical evidence on the dissemination of, engagement with, and impact of ‘fake news’ and other malinformation in public debate, in Australia and beyond. It takes a triangulated approach, combining …
- Study level
- PhD
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Communication
- Research centre(s)
- Digital Media Research Centre
Understanding public perceptions of the sustainable energy transition: a social network analysis
The challenge to keep global warming to 1.5°C above pre-industrial levels has become even greater due to a continued increase in greenhouse gas emissions (IPCC, 2023). One major challenge is the shift from fossil fuels to renewable energy to reduce emissions (Gholami et al., 2016). The share of renewable energy in electricity generation has increased to 28.3%, however, an acceleration of the pace of the transition is required to limit global temperature rise (REN21, 2022).In this project we investigate public …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Modelling and managing uncertain Antarctic species networks
Antarctic ecosystems are complex, and data is limited since it is expensive to collect. Species interact in food webs which can be modelled as mathematical networks. The relationships between species are not always known, or we might know they interact but not how strongly. Noisy (or imperfect) data can be used to model these species interactions to give more certainty about how the ecosystem works as a whole – although the worse the data is, the less information it contributes. …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Centre for the Environment
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