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 52 matching student topics
Displaying 1–12 of 52 results
Bridging Real-World Modelling and Secondary Mathematics Education
This project explores how authentic modelling approaches (for example Bayesian reasoning and systems thinking) can be integrated into secondary mathematics classrooms to enhance engagement and conceptual understanding.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Education
Water living lab: flood modelling and visualisation
Smart use of rich data sets and state-of-the-art models in a central framework provides opportunities to address problems that were previously out of reach. This is particularly true in managing and responding to flood scenarios where an integrated platform can gather forecasted and measured weather and streamflow data and use those data in foresting systems, enabling an integrated visualisation platform for data sharing and real-time decision making.The water engineering research team is developing analytical and visualisation frameworks that can support …
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
From feedback loops to actionable insights: system dynamics modelling for sustainable energy systems
Energy systems are becoming increasingly complex, shaped by fluctuating renewable supply, evolving user behaviour, and dynamic market structures. To navigate this complexity, system dynamics (SD) modelling (Sterman, 2000) offers a powerful lens to understand and influence the behaviour of energy systems over time. By visualising and simulating feedback loops, stock–flow structures, and interdependencies (Fang et al., 2018), SD modelling can help generate actionable insights for designing resilient, adaptive, and user-centred energy solutions.This project explores how SD modelling can support innovation …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
-
Energy Transition Centre
Basic aircraft collision risk modelling and visualisation
Aircraft collision risk modelling is complex yet key to ensuring safe air transport (both crewed and uncrewed aircraft). Different collision risk models are better suited to different airspace environments which means model comparison and evaluation is an important research problem. This project takes a deeper look into a specific collision risk modelling approach: gas models.
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
Overcoming the challenges of sensitive data via synthetic data generation (case study)
In the 21st Century, there is an abundance of data, often containing insights that could benefit a number of stakeholders. However, despite this opportunity, it is often the case that the data is sensitive and can not be released by organisations or government agencies due to privacy concerns. One possible solution to the above dilemma is to instead carefully construct a 'twin' data set that contains similar information (and ideally, the same insights) as the original data set, but without …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Optimal conservation management in uncertain Antarctic environments
Species and ecosystems in Antarctica are threatened. Optimal biodiversity conservation is an interdisciplinary field combining mathematical modelling and optimisation with ecology and conservation. We can use mathematics to understand the system, model how management actions might impact it, and then optimise which actions should be used. For example, we can explore where protected areas should be placed, how species should be managed, or how tourist impacts should be reduced. However, the complexities of conservation in Antarctica necessitate the application of …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Centre for the Environment
Modelling and managing uncertain Antarctic species networks
Antarctic ecosystems are complex, and data is limited since it is expensive to collect. Species including penguins, seabirds, invertebrates, mosses, and marine species interact in food webs which can be modelled as mathematical networks. These networks can be large, span across terrestrial and marine systems, and are changing in response to environmental changes.These ecological networks can be modelled using differential equation predator prey models like Lotka-Volterra to describe these interactions. However, the relationships between species are not always known, or …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Centre for the Environment
Optimal ecosystem management in rapidly changing systems
Delays in acting in collapsing ecosystems can be catastrophic. With every passing year, the chances that the ecosystem has progressed past some point of no return increases. Yet the research and development needed to develop a new technology can take a long time. Balance between these two dynamic processes is needed to determine the optimal length and effort for developing new technologies. This project will develop a method for finding the optimal schedule for developing technological readiness, social acceptability, a …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Centre for the Environment
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
Using mathematics to understand multiple sclerosis: what causes the immune system to attack the brain?
Every day, we use our bodies to move, think, talk and eat, but for people with multiple sclerosis (MS) these tasks can be virtually impossible. MS is a chronic disease which develops because the immune system has started to attack the nerve cells in the brain. This causes the degradation of parts of the brain and irreversible impairment in physical and mental activity. Unfortunately, this disease has no cure, and while considerable therapeutic advances against this disease have been achieved, …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
Modelling educational equity and access pathways
This project examines how pathways of disadvantage and success develop across schooling. It focuses on identifying how socio-economic, wellbeing, and contextual factors interact over time to shape outcomes.This project extends ongoing research into student engagement trajectories and aligns with faculty priorities in inclusive and socially just education.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Education
Modelling student engagement and success using Bayesian networks
This PhD project investigates how academic, wellbeing, family, and school-context factors interact to shape student engagement, retention, and academic success in secondary schooling. Using large-scale longitudinal datasets, the project will develop advanced probabilistic models to identify key predictors and pathways that explain diverse learner trajectories.The research will contribute to evidence-based strategies for improving student engagement and reducing attrition, with strong relevance to policy and practice in Australian and international contexts.This project builds on ongoing work applying advanced statistical modelling to …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Education
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