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 476 matching student topics
Displaying 25–36 of 476 results
Explainable AI-enabled predictive analytics
Modern predictive analytics underpinned by AI-enabled learning (such as machine learning, deep learning) techniques has become a key enabler to the automation of data-driven decision making. In the context of process monitoring and forecast, predictive analytics has been applied to making predictions about the future state of a running process instance - for example, which task will be carried out next, when and who will perform the task, when will an ongoing process instance complete, what will be the outcome …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Investigating factors impacting urban heat vulnerability in subtropical cities
In recent years, with the rise in climate change impact, urban heat has become a major issue for many cities to tackle consequently. Extreme heat events are becoming more frequent and intense due to climate change, which has directly caused a substantial increase in heat-related morbidity and mortality. This indispensably puts an extra burden on medical systems and national finance. Meanwhile, the urban heat island effect has been exaggerating the consequences caused by the increased extreme heat in metropolitan areas. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
The dark side of robotic process automation
Pandemics such as COVID 19 have forced organisations to pursue hyper-automation to maintain operational sustainability. Many organisations are keen to adopt Robotic Process Automation (RPA) to dramatically improve operational efficiency. However, evidence to date highlighted various associated challenges associated with adoption of RPA in organisations.Furthermore, recent surveys by consultant organisations found a high RPA project fail rate and their inability to meet the expected return on investment.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Future Enterprise
Alleviating corruption: a data driven perspective
Corruption is cited as among the greatest challenges faced by government and citizenry the world over and threatens to undermine the very trust that is essential for a functioning democratic society. In order to earn and maintain public trust, governments at all levels must continuously strive to reduce corruption and uphold the highest levels of integrity.Amidst the countless human interactions and electronic transactions that occur within the public service on a daily basis are a complex and ever-changing variety of …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Understanding responsible deployment of computer vision for urban planning
Advances in artificial intelligence (AI) offer urban planning practice many novel prospects. By the responsive use of AI, planners can effectively analyse data, improve processes, increase efficiency, and prioritise human-centric aspects of planning to develop sustainable cities. Computer vision is one of the key areas where responsible AI is applied in urban planning to revolutionise the analysis and interpretation of visual data, like images and videos captured in cities to aid decision and plan making processes. While the potential impacts …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
Parameter identifiability for stochastic processes in biological systems
Stochastic models are used in biology to account for inherent randomness in many cellular processes, for example gene regulatory networks. Noise is often thought to obscure information, however, there is an increasing understanding that some randomness contains vitally important information about underlying biological processes.When applying these models to interpret and learn from data, unknown parameters in the model need to be estimated. However, not all data will contribute to a given estimation task regardless of the data quantity and quality. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Atmospheric cooling and shading for the Great Barrier Reef
This research sits within the Cooling and Shading Subprogram of the Reef Restoration and Adaptation Program (RRAP).RRAP is an ambitious and innovative R&D effort that places Australia as the leader of coral reef adaptation and restoration science. It is a consortium of Partners, including QUT, dedicated to creating an innovative toolkit of interventions to help the Reef resist, adapt to, and recover from the impacts of climate change. These partners include the Australian Institute of Marine Science, CSIRO, the Great …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Earth and Atmospheric Sciences
- Research centre(s)
-
Centre for the Environment
Time-series analysis of 2D diffraction patterns for Synchrotron rock physics
The interaction between deformation, fluid flow, chemical reactions, and heat flow in rocks constitutes a research frontier in the Earth Sciences. In addition to fundamental academic interest in this subject, there are many applied industrial problems, which require a sound understanding of this coupling. Examples include: the long-term sequestration of carbon dioxide in rocks, the energy-efficient processing of future-mineral resources, the design of unconventional geothermal-energy operations, and the prediction of earthquakes and volcanic eruptions.The advisory team pioneered new methods for …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Earth and Atmospheric Sciences
- Research centre(s)
- Centre for Data Science
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
Place-based giving and philanthropy
Effective models of place-based funding remain conceptually unresolved. Place-based, collective impact initiatives are increasingly recognised for creating long-term systems change, yet the role of philanthropy in supporting, advocating for and catalysing change is underexplored.I am interested in supervising research into conceptual models of philanthropic funding for place-based initiatives, to explain and clarify the elements and characteristics of successful, long-term relationships between philanthropic funders and place-based, community-led initiatives in regional and urban Australian communities.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Business and Law
- School
- School of Accountancy
- Research centre(s)
-
Australian Centre for Philanthropy and Nonprofit Studies
Scalable Bayesian Inference using Multilevel Monte Carlo
Bayesian inference is a popular statistical framework for estimating the parameters of statistical models based on data. However, Bayesian methods are well known to be computationally intensive. This fact inhibits the scalability of Bayesian analysis for real-world applications involving complex stochastic models. Such models are common in the fields of biology and ecology.Multilevel Monte Carlo (MLMC) methods are a promising class of techniques for dealing with the scalability challenge. These approaches use hierarchies of approximations to optimise the trade-off between …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Coarse-grained molecular dynamics modelling in expansive soil
Expansive soil/active soil has wide applications in geotechnical engineering and other engineering disciplines due to its desirable special properties - for example, low permeability and swelling pressure under saturated condition. But these materials are highly susceptible to experiencing huge volume change and even damage due to moisture content reduction. However, the underlying mechanism of this phenomenon is still not clear for geotechnical engineers. Therefore, there is no optimum solution available to solve the problem.In this project, a special modelling approach …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Materials Science
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