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 72 matching student topics
Displaying 61–72 of 72 results
Learning complex dynamics from multimodal time-series data
Modelling non-stationary dynamics from high-frequency time-series data remains challenging. These signals often exhibit complex temporal and spectral structure, while observations are typically noisy, incomplete, and affected by changing operating conditions, making reliable prediction and representation learning difficult.This PhD project, offered at Queensland University of Technology (QUT) in collaboration with industry partners, focuses on learning representations and dynamics from multimodal time-series data.The research will explore deep approaches including sequence models, transformer-based architectures, anomaly detection, graph neural networks, and self-supervised learning, with …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Modelling student engagement and success using Bayesian networks
This PhD project investigates how academic, wellbeing, family, and school-context factors interact to shape student engagement, retention, and academic success in secondary schooling. Using large-scale longitudinal datasets, the project will develop advanced probabilistic models to identify key predictors and pathways that explain diverse learner trajectories.The research will contribute to evidence-based strategies for improving student engagement and reducing attrition, with strong relevance to policy and practice in Australian and international contexts.This project builds on ongoing work applying advanced statistical modelling to …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Education
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
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
Mathematical modelling of brain cancer informed by patient data
In this research project, you will develop a mathematical model, known as an agent-based model, to capture the development of a brain cancer in a patient. The model will then be matched to clinical samples from patients and used to make predictions around treatment efficacy.
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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)
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Centre for Microbiome Research
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
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
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
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
Emerging technologies and data-driven learning in engineering education
This research topic explores the use of emerging technologies and data‑driven approaches to enhance learning and teaching in engineering education. The project investigates how diverse educational data sets can be leveraged to support evidence‑based decision making across multiple levels of the institution—from individual educators and course teams to faculty leaders and senior executives. The work sits at the intersection of engineering education, learning analytics, and strategic use of educational data to improve student engagement, experience, and success.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Data Science
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