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 468 matching student topics
Displaying 25–36 of 468 results
Surface engineering for nanoelectronic devices
Ga2O3 is an emerging wide-bandgap semiconducting material that has received enormous attention in recent years. This is due to its potential application in power devices, UV detectors and military applications that are unattainable by conventional semiconductors such as silicon.The operation and performance of these type of electronic devices rely critically on the surface quality and properties of the semiconducting materials. However, the surface atomic structures and electronic structures of Ga2O3 single crystals are not yet fully understood.The principal aim of …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Centre for Clean Energy Technologies and Practices
Driver engagement and risk in automated driving: Advanced data analytics leveraging driver monitoring systems
The project aims to the explore concept of empathic machines in the context of driver monitoring systems (DMS) and automated driving. The successful candidate will contribute to advancing the understanding of driver engagement, situation awareness, and risk through leveraging advancements in data science techniques on vehicle sensor, DMS, and other related datasets.To apply for this position, please submit the following documents:a cover letter outlining your research interests, relevant qualifications, and motivation to join the Empathic Machines projecta detailed curriculum vitae …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Data Science
Centre for Future Mobility
Optical coherence tomography imaging of arterial tissue
The sudden rupture of vulnerable atherosclerotic plaques and subsequent thrombosis formations are responsible for most acute vascular syndromes, such as myocardial infarction and stroke. Many victims who are apparently healthy die suddenly with no prior symptoms.Such deaths could be prevented through surgery or alternative medical therapy, if vulnerable plaques were identified earlier in their natural progression.While intravascular methods have been developed to visualize various features of vulnerable plaques, there is no single technique that can accurately predict plaque rupture in …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
Image-based computational model to predict intracranial aneurysm rupture
Intracranial aneurysms are bulging, weak areas of an artery that supply blood to the brain which are relatively common. While most aneurysms do not show symptoms, 1% spontaneously rupture which can be fatal or it can leave the survivor with permanent disabilities. This catastrophic outcome has motivated surgeons to operate on approximately 30% of aneurysms despite their rate of complications arising and cost of operation.The impact of aneurysm morphology on blood flow shear stress and rupture could educate surgical decision-making …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
Structural application of green concrete
The need for sustainable construction has prompted researching alternative concrete technologies around the world. In QUT, a project has been developed to investigate structural applications of environmentally friendly (Green) concrete.Project activities can be undertaken by students at various levels, including VRES, final-year undergraduates, and PhD researchers.
- Study level
- PhD, Honours
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Materials Science
Centre for the Environment
A new physics informed machine learning framework for structural optimisation design of the biomedical devices
The machine learning based computer modelling and simulation for engineering and science is a new era. The optimisation analysis is widely used in the design of structures.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
Centre for Biomedical Technologies
Adaptive and efficient robot positioning
I am looking for highly motivated and talented PhD students to work with us on robot localisation and navigation. The students would join my DECRA Fellowship project "Adaptive and Efficient Robot Positioning Through Model and Task Fusion" funded by the Australian Research Council, which provides substantial top-up scholarships in addition to QUT's tax-free base stipend.Robot positioningWhere are you? This is a fundamental question to which most of us usually know the answer. And so do the birds singing in our …
- Study level
- PhD
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
Advanced materials for redox flow batteries
Grid-scale energy storage for intermittent renewables like solar and wind is an essential element of the transition away from fossil fuel based electricity production. Redox flow batteries have some very interesting characteristics for this stationary storage application:they are safer than other battery typesthe amount of energy stored can typically be scaled up easilythe power and energy of a system are more decoupled compared to lithium and other batteries, making them flexible in their design parameters.Ion exchange membrane and electrode are …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Evaluation of language models and word embedding methods for natural language processing applications
In deep learning models, language models and word embedding methods have become popular to understand the context of text data. There exist many variants of these methods and have different limitations. This project will introduce you to the hot topic of language models and the fields of Natural Language Processing and Text Mining.
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Curvature dependence of reaction-diffusion wave front speed with nonlinear diffusion.
Reaction-diffusion waves describe the progression in space of wildfires, species invasions, epidemic spread, and biological tissue growth. When diffusion is linear, these waves are known to advance at a rate that strongly depends on the curvature of the wave fronts. How nonlinear diffusion affects the curvature dependence of the progression rate of these wavefronts remains unknown.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
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
Automatic Generation of Software Vulnerability Datasets for Machine Learning
In recent years, machine learning has enjoyed profound success in a range of interesting applications such as natural language processing, computer vision and speech recognition. It has been possible mainly due to, in addition to better computing resources, the availability of large amounts of training datasets to these applications. However, in software security research, the lack of large datasets is an open problem that makes it challenging for machine learning to reason about security vulnerabilities found in real-world software. The …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Computer Science
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