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 28 matching student topics
Displaying 1–12 of 28 results
Capturing the impact of patient variability in a novel cancer treatment
In 2015, the Food and Drug Association (FDA) approved a lab-engineered virus for the treatment of melanoma (skin cancer). Since then, there has been a significant increase in the number of lab-grown viruses that are being tested in clinical trials as potential treatments of cancer. Unfortunately, it seems that a large number of patients in these clinical trials fail under this treatment and currently there is no way to distinguish between responders and non-responders to treatment.Fortunately, we can use mathematics …
- Study level
- Honours, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Presenting the Australian Cancer Atlas using different geographic structures
The Australian Cancer Atlas (ACA) is an award-winning and globally recognised tool for understanding the spatial variation in cancer incidence and survival across Australia. For statistical rigour, the ACA currently presents estimates of relative risk by “SA2”, an area structure defined by the Australian Bureau of Statistics that groups together similar subpopulations. However, many health organisations use different areal structures, such as Primary Health Networks (PHN) or electoral boundaries.The goal of this project will be to develop methods for summarising …
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Investigating the utility of hex maps to communicate information for the Australian Cancer Atlas
The Australian Cancer Atlas (ACA) is an award-winning and globally recognised tool for understanding the spatial variation in cancer incidence and survival across Australia. Currently in the ACA, the areas where the majority of the Australian population live (ie areas in cities), are not immediately visible in the Atlas. The goal of this project is to develop hex visualisations of the ACA estimates.The student may work either at QUT (GP or KG), at Cancer Council Queensland or both, depending on …
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Predicting how fast the next Olympic medalists will swim
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) is currently under way. 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
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Genetics of cardiovascular disease
This research project involves investigating the genetic basis of cardiovascular disease (CVD). The project will focus on the genetically unique population of Norfolk Island. The Norfolk Island Health Study has been running for 20 yrs. Over this time the cardiovascular health of the Islanders has been tracked via the collection of relevant clinical data. In addition whole genome sequence data from the study group has been collected, which will facilitate the discovery of genetic variants that influence CVD phenotypes - …
- Study level
- PhD
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
- Centre for Genomics and Personalised Health
Big Data ideas for GLMs
The goal of this project is to develop new Bayesian methods for large-scale data analysis using subsampling techniques. The focus of the project will be on generalised linear models (GLMs), which are commonly used models in statistics and machine learning.One of the main challenges in using Bayesian statistics with big data is the high computational cost associated with processing big datasets. The proposed project aims to address this challenge by developing new subsampling techniques for Piecewise Deterministic Markov Process (PDMP) …
- Study level
- Master of Philosophy, Honours, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Predicting player performance from one format to another in cricket
Identifying talent as early as possible in elite sport is critical. An important component of this is learning about what metrics of performance in lower grades to focus on to help predict performance in the top grade. This project will explore for this research problem for cricket.
- Study level
- Master of Philosophy, Honours, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- 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, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Where’s the confusion? And can we make sense of it?
Confusion matrices characterise the performance of classification systems on training and test data, but they can be hard to make sense of, especially when there are many possible classes to which an example could be assigned.We have developed confusR a method to visualise confusion matrices and make distinct the contribution of the classifier and the contribution of the prior abundance of different classes. We have also recently developed approaches for visualising uncertainty in confusion matrices and the performance metrics derived …
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Resolving uncertainty in decisions to improve agri-food system outcomes for people and nature
Despite efforts to monitor and manage declining species and ecosystems around the world, biodiversity is still not routinely included in mainstream decision-making and continues to decline at the highest rate in human history. Added to this is the problem that both natural and agri-food systems are complex networks that are continually changing due to human and natural disturbances, with climate change likely to increase the impacts of extreme events like drought, fire and economic shocks on these networks.Because of large …
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Biology and Environmental Science
- Research centre(s)
- Centre for Data Science
Centre for the Environment
Statistical methods for detecting Antarctic ecosystems from space
Satellite images are a frequent and free source of global data which can be used to effectively monitor the environment. We can see how the land is being used, how it’s being changed, what’s there – even where animals are in the landscape. Using these images is essential, particularly for regions where data is expensive to collect or difficult to physically access, like Antarctica. In Antarctica and the sub-Antarctic islands, satellite images can be an easy and quick way to …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Centre for the Environment
Harnessing the Power of Data Science to Protect Endangered Fish Populations
In this research project, we explore the world of endangered fish species in Alberta, Canada, aiming to gain a deep understanding of aquatic ecosystems. Our focus is to assess the factors impacting the abundance of endangered trout populations, a topic of great interest among scientists. Through the application of advanced statistical machine learning models, we analyze parameters measured by water sensors to uncover the factors affecting fish populations. By developing a predictive framework, we aim to provide valuable insights into …
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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