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.

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Found 446 matching student topics

Displaying 25–36 of 446 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/CARRSQ

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

Making the most of many models

In the age of Big Data, machine learning methods, and modern statistics the adage "all models are wrong but some are useful" has never been so true. This project will investigate data science approaches where more than one model makes sense for the data. Is it better to choose a single model or is there something to be gained from multiple models?This project will look at variable selection methods, penalised regression, Bayesian model averaging and conformal prediction. The research has …

Study level
Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

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

Representation learning for anti-microbial resistance

This project is about using neural network models help us understand Anti-Microbial Resistance (AMR), a phenomenon in which bacteria adapt to reduce the effectiveness of antibiotics, usually through a process known as Lateral or Horizontal Gene Transfer - where genes are included in the organism from other sources.Our focus will be on learning compact vector representations of biological sequences known to be associated with AMR genes. By encoding DNA sequences in this way we can more rapidly identify AMR genes …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Scalable software solutions for improving the CRISPR gene editing system

The CRISPR-Cas9 technology allows the modification of virtually any gene in any organism of interest. It has generated a lot of interest, both in the research community and the general population.One of the crucial components of CRISPR experiments is the design of the 'guide RNAs' that will control where modifications occur. We have developed a software pipeline, named Crackling, to identify safe and effective guide RNAs across entire genomes.We are seeking to expand and improve various aspects of our current …

Study level
Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Simulation of turbulent fluid flow through a microfluidic device using CFD

Microfluidic devices (MFD) are extensively used in microbial studies. Bacterial cell attachment onto surfaces under flow conditions in laminar regime has been previously studied using a custom designed MFD.As an extension of this study, microbial attachment under turbulent flow is to be studied in a future project. The suitability of current MFD for microbial studies under turbulent flow must be evaluated to adopt / redesign the MFD.A computational fluid dynamics (CFD) analysis is proposed to examine the fluid flow inside …

Study level
Honours
Faculty
Faculty of Engineering
School
School of Mechanical, Medical and Process Engineering

Sport AI

Videos of sport activities are widely available at large scales. AI and its sub-fields, especially computer vision and machine learning, have a great potential to analyse, understand and extract useful information from these videos.This project aims at using AI and its subfields in computer vision and machine learning to develop techniques for analysing sport videos to extract intelligence for players and coaches.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

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