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 7 matching student topics
Displaying 1–7 of 7 results
Energy transitions in the food sector: how AI shapes consumer behaviour over time
Energy transitions are often studied in sectors such as transport (e.g. electric vehicles) and housing (e.g. solar panels, batteries), where decisions are relatively infrequent, highly deliberative, and associated with clear long-term payoffs. In contrast, food consumption represents a fundamentally different energy-relevant sector: decisions are made daily or even multiple times per day, involve low deliberation, and prioritise immediate outcomes such as convenience, cost, and taste (Reisch, 2021). These characteristics make food systems particularly susceptible to short-term decision-making, where long-term energy …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Future Enterprise
Energy Transition Centre
Evidence-driven policy innovation for urban heat islands
Extreme heatwaves and other extreme weather events are contributing to the fragility of cities and urban infrastructure, which requires urgent attention. Urban heat islands are an exemplar for metropolitan fragile areas, which exacerbate the impact of climate change and global warming on natural hazards, such as wildfires, storms, floods, and droughts, which pose a critical threat to Australian and international communities (Degirmenci et al., 2021). Decision support systems (DSS) can help city planners and policymakers to optimise their decision-making by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Business and Law
- School
- School of Management
- Research centre(s)
- Centre for Future Enterprise
Digital business models
The ongoing proliferation of a digital lifestyle and the exposure to global innovation raises the expectation level of customers for new products and services. The ongoing technological innovations make this possible, but require digital business models to turn them into viable commercial offerings.This is not only relevant for new (‘born-on-the-web’) companies, but also for traditional companies who run the risk of having their core business disrupted. While some struggle with the new business models (e.g. Kodak), others turn it into …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Future Enterprise
Business model innovation
Business models are becoming an important focus of innovation activities of practitioners and innovation studies of academics.When organisations want to introduce new products or services or need to transform their existing products or services, a viable business model is a critical requirement for commercial success. For example, many internet services are provided for free and require providers to find alternative sources of revenue, such as advertisements or premium services.In addition, the innovation of the business model itself is often a …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Future Enterprise
Human-in-the-loop techniques to debug machine learning models
Machine learning models are being deployed in critical domains such as healthcare, education and fintech. The current approach to deploying machine learning models is based on considering a data-centric approach where the models are evaluated using performance measures on a test set. However, the high performance of the model on test data is not indicative of its reliability,An important aspect of reliability is in the understanding of what exactly a machine learning model encodes, and to verify if it learns …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Gamified process-data cleaning
Despite the importance of data quality, it is often compromised. The majority of the time and energy in most data science projects is spent on data cleaning. Process-oriented data mining (process mining) is not an exception. A recent process mining survey shows that more than 60% of the time and effort is spent on data transformation and pre-processing.This research project aims to systematically analyse the quality of process data generated within organisational workflows, with the goal of identifying common data …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Mathematical modelling of ecosystem feedbacks and value-of-information theory
Ecosystems respond to gradual change in unexpected ways. Feedback processes between different parts of an environment can perpetuate ecosystem collapse, leading to potentially irreversible biodiversity loss. However, it is unclear if greater knowledge of feedbacks will ultimately change environmental decisions.The project aims to identify when feedbacks matter for environmental decisions, by generating new methods that predict the economic benefit of knowing more about feedbacks. Combining ecological modelling and value-of-information theory, the outcomes of these novel methods will provide significant and …
- 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
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