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 70 matching student topics
Displaying 61–70 of 70 results
Providing legal evidence for non-accidental scald burn injury
Although most burn injuries are completely accidental in nature, they can also occur due to neglect or abuse. Burn clinicians are often required to ascertain if the patient history and the wound are consistent with accidental or non-accidental injury. If the case goes to court, the clinician will prepare a medico-legal report as evidence. We have previously conducted studies examining the depth of burn injury after different durations and temperatures of hot water. This data can be used to predict …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
Process-data governance patterns
Data is recognised a strategic asset for organisations. There is a growing need to manage the voluminous data an organisation is exposed to in order to use it for decision-making.Of particular significance is process data, which consists of information about the execution of processes. Such information is used to uncover behaviour of processes within an organisation. This brings forth the significance of data governance. Data governance is the exercise of control and authority over management of data. Despite its significance, …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
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
Developing predictive models, methods and analytics for complex sports data
A 3-year strategic partnership on sports data science between the Centre for Data Science (CDS), the Australian Institute of Sport (AIS) and the Queensland Academy of Sport (QAS) was launched in the past few months. With a drive towards data informed decision making across the high performance sports network nationally, a number of collaborative, interdisciplinary research and scholarship opportunities ranging from VRES, to honours, masters and PhD have developed.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Virtual leaves: from data to surfaces and the steps in-between
Like all industries, agriculture is benefiting from the data and computing revolution. Using hand-held scanners, CT scanners, or other technologies, we can acquire data sets that represent real leaves of agricultural crops, e.g. wheat. Using this data, and performing many intermediate steps, we can build virtual leaf surfaces that can be used in computer models to perform simulations of droplet impactions, spreading, evaporation, and other phenomena of interest to the industry.This project concerns the 'many intermediate steps', for which there …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Measuring higher education performance: a global comparisons using network data envelopment analysis
The research objective focuses on comparing the top 100 universities (according to the Times Higher Education) from 2010 to 2020. The objective of the project is fourfold. First, to derive appropriate research outputs per university. Second, employ a Network DEA approach to identify (in)efficiencies within the network. Third, to measure productivity change of universities using the Fare-Primont index. Fourth, to determine sources of (in)efficiencies and productivity.This project is both theoretical and applied. The applicant should possess strong mathematical and computational …
- Study level
- PhD
- Faculty
- Faculty of Business and Law
- School
- School of Economics and Finance
Enhancing 3D visual understanding through multimodal data fusion
The demand for 3D scene understanding through point clouds is rapidly growing in diverse applications, including augmented and virtual reality, autonomous driving, robotics, and environment monitoring. However, the field faces challenges due to limited data availability and predefined categories. Training deep 3D networks effectively for sparse LiDAR point clouds requires significant amounts of annotated data, which is both time-consuming and expensive. Building on the advancements in 2D models that leverage the power of image and language knowledge, our project aims …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Lunar seismology: Using lunar seismology data for site characterisation at Schrodinger crater
QUT is involved in the science team for a recently Australian Space Agency-funded mission to Schrodinger crater, to deploy a Fleet Space seismometer. QUT is developing workflows to translate the seismic data into detailed subsurface models for site characterisation, off-world construction, and in-situ resource mapping of materials such as ice.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Earth and Atmospheric Sciences
- Research centre(s)
- Centre for Data Science
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
Novel algorithms for microbiome data
Metagenomics data is complex, high-volume data and keeps evolving, requiring novel computational method development as the wetlab approaches changes and databases grow. Thus, novel computational methods are required to take advantage of them.There are several potential projects under this topic, including:using deep learning to improve metagenomics assemblydeveloping better tools to analyse the presence of resistance genes in metagenomics datadeveloping approaches for estimating the quality of genomes from novel generation sequencespredicting the function of small sequences using more than just sequence.Interested …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
-
Centre for Microbiome Research
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