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 26 matching student topics
Displaying 1–12 of 26 results
Advanced artificial intelligence based ultrasound imaging applications
Our research in the space of advanced quantitative medical imaging is investigating how to use ultrasound as a real time volumetric mapping tool of human tissues, to guide in a reliable and accurate way complex medical procedures1. We have developed several novel methods which make use of the most cutting-edge artificial intelligence technology2. For example, to show where the treatment target and the organs at risk are at all times during treatments in radiation therapy3, 4; or to inform robots …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Clinical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
Technology, Innovation and Health
Professor Belinda Bennett is interested in talking to students who wish to undertake research on legal issues related to technology, innovation and health, regulation of innovative health technologies, legal issues related to genomics, the use of artificial intelligence in health care, and the use of robotics in health care.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Business and Law
- School
- School of Law
- Research centre(s)
-
Australian Centre for Health Law Research
New technology and the law
Computer vision has developed to a point where machines using artificial intelligence are better and faster than humans at performing many vision-related tasks. For example, we are now often processed through customs based solely on face recognition software. Add to this the fact that the average Australian is photographed on CCTV cameras around 75 times per day. Commercial applications of face recognition technology include Microsoft's Face Application Programming Interface that can be used to classify face images based on gender, …
- Study level
- PhD, Master of Philosophy
AI, data, and mathematical thinking in education
This project explores how emerging technologies, including artificial intelligence, influence mathematical thinking, teaching, and learning. It focuses on how students and teachers engage with data-rich and AI-supported environments.The project aligns with ongoing work in quantitative reasoning, modelling, and educational innovation, including research on adaptive learning technologies.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Education
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
Explainable AI for medical imaging
AI is increasingly used for interpreting medical images (e.g. MRI, CT, X-ray) in order to diagnose or monitor diseases. We are working on methods that can explain the AI decision and provide supplementary information. For example, if AI detect an abnormality, we want to generate the same scan without the abnormality. Another example is to detect automatically an area that is suspicious just by learning what healthy scans look like.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Leveraging AI-driven cognitive computing for energy systems innovation
The transition toward a more sustainable energy system is generating vast volumes of data from distributed sources such as smart meters, energy sensors, and user-end devices. Energy informatics highlights the crucial role of information systems in optimising both energy supply and demand (Watson et al., 2010). In this project, we explore how cognitive computing systems (CCS), integrating artificial intelligence (AI), cognitive psychology, and neurobiology, can strategically transform energy informatics by creating adaptive, explainable, and human-aligned energy solutions.Leveraging advances in CCS …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
-
Energy Transition Centre
Multimodal AI to simulate medical student competency
The assessment of medical graduate competency is a cornerstone of medical education and a critical safeguard for patient safety. Newly qualified physicians must demonstrate a broad range of skills and knowledge, including diagnostic reasoning, clinical decision-making, communication, procedural skills, and professionalism before independently practicing medicine. Traditional assessment methods often include standardized multiple-choice examinations, objective structured clinical examinations (OSCEs), direct observation of procedural skills (DOPS), and portfolio reviews. While these methods offer valuable insights, they have inherent limitations. Standardized tests may …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Public Health and Social Work
- Research centre(s)
- Centre for Data Science
Scene Understanding for Underwater Imagery
Underwater ecosystems, including coral reefs and seagrass meadows, play a critical role in maintaining marine biodiversity, providing coastal protection, and supporting fisheries and tourism economies that millions depend upon globally. These habitats are increasingly vulnerable to climate change, pollution, and other anthropogenic impacts, demanding urgent efforts to monitor and restore them. Accurate scene understanding of underwater imagery enables fine-scale ecosystem monitoring across spatial and temporal scales, supporting essential activities such as habitat and biodiversity assessment, validation of aerial and remotely …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
Bridging the gap: leveraging AI to improve healthcare access
Access to quality healthcare remains a significant challenge in many parts of the world, often due to geographic and financial barriers. This research explores how artificial intelligence (AI) can address the challenges of geographic and financial barriers in accessing healthcare. The project will focus on developing AI-powered solutions that enhance healthcare delivery, increase patient engagement, and reduce costs
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Public Health and Social Work
Enhancing clinical decision-making through AI-assisted agents
Artificial Intelligence (AI) has shown tremendous potential in revolutionizing healthcare delivery. This research focuses on developing AI agents that can augment clinical decision-making processes, ultimately improving patient outcomes. The project aims to explore and design novel AI architectures that integrate disparate medical data sources, providing context-aware recommendations for diagnosis, treatment planning, and care coordination. Despite the promising applications of AI in healthcare, significant challenges remain in integrating these technologies into clinical practice effectively and safely.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Public Health and Social Work
Development of a machine learning algorithm for high throughput cell response data in drug therapy
High-throughput screening assays are essential for accelerating drug discovery, but current assays often rely on endpoint measurements that do not capture the dynamic response of cells to drug treatment. Machine learning algorithms (MLAs) have the potential to enable real-time, high-throughput monitoring of cell response to drug treatment by analyzing complex datasets generated by multiplexed live-cell assays. This research project aims to develop an MLA for enabling high throughput cell response data in drug treatment. The project will involve three main …
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Computer Science
- Research centre(s)
- Centre for Biomedical Technologies
Centre for Biomedical Technologies
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