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 25 matching student topics
Displaying 13–24 of 25 results
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
Scalable Bayesian Inference using Multilevel Monte Carlo
Bayesian inference is a popular statistical framework for estimating the parameters of statistical models based on data. However, Bayesian methods are well known to be computationally intensive. This fact inhibits the scalability of Bayesian analysis for real-world applications involving complex stochastic models. Such models are common in the fields of biology and ecology.Multilevel Monte Carlo (MLMC) methods are a promising class of techniques for dealing with the scalability challenge. These approaches use hierarchies of approximations to optimise the trade-off between …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Surrogate models for accurate prediction and inference in mathematical biology
High fidelity mathematical models of biological phenomena are often complex and can require long computational runtimes which can make computational inference for parameter estimation intractable. In this project we will overcome this challenge by working with computationally simple low fidelity models and build a simple statistical model of the discrepancy between the high and low fidelity models. This approach provides the best of both worlds: we obtain high accuracy predictions using a computationally cheap model surrogate.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Branching processes, stochastic simulations and travelling waves
Branching processes are stochastic mathematical models used to study a range of biological processes, including tissue growth and disease transmission.This project will implement a simple stochastic branching process to generate simulations of biological growth, and then consider differential equation-based description of the stochastic model.Using computation we will compare the two models, and use phase plane and perturbation analysis to analyze the resulting traveling wave solutions.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Digital publics
Digital and social media platforms provide new opportunities for public communication, and the formation of distinct publics and communities around shared interests and identities. Such publics may engage in political debate, popular media fandom, science communication, vernacular creativity, and other activities; but they may also be affected by, or actively engage in promoting, mis- and disinformation and other problematic content. Their activities are also shaped by the features and affordances of the platforms they use, from Facebook and Twitter to …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Communication
- Research centre(s)
- Digital Media Research Centre
Mathematical and computational models for diffusion magnetic resonance imaging (dMRI)
In 1985, the first image of water diffusion in the living human brain came to life. Since then significant developments have been made and diffusion magnetic resonance imaging (dMRI) has become a pillar of modern neuroimaging.Over the last decade, combining computational modelling and diffusion MRI has enabled researchers to link millimetre scale diffusion MRI measures with microscale tissue properties, to infer microstructure information, such as diffusion anisotropy in white matter, axon diameters, axon density, intra/extra-cellular volume fractions, and fibre orientation …
- 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 Biomedical Technologies
Topics in computational Bayesian statistics
Bayesian statistics provide a framework for a statistical inference for quantifying the uncertainty of unknowns based on information pre and post data collection.This information is captured in the posterior distribution, which is a probability distribution over the space of unknowns given the observed data.The ability to make inferences based on the posterior essentially amounts to efficiently simulating from the posterior distribution, which can generally not be done perfectly in practice.This task of sampling may be challenging for various reasons:The posterior …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Mathematical and computational techniques for advection diffusion reaction models
Mathematical models of advection diffusion reaction processes are fundamental to many applied disciplines including physics, biology, ecology and medicine. This project will focus on developing mathematical and computational techniques for continuum (PDE) and/or stochastic (random walk) models of advection diffusion reaction.Potential project topics include:building new simplified models that are easier to implement, interpret and analyseextracting new mathematical insights into advection diffusion reaction processesproposing new methods for parameterising models from datadeveloping new numerical and/or analytical methods for solving PDE models.All project …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
Image-based assessment of atherosclerotic plaque vulnerability: Towards a computational tool for early detection and prediction
Plaque characteristics and local haemodynamic/mechanical forces keep changing during plaque progression and rupture.Quantifying these changes and discovering the progression-stress correlation can improve our understanding of plaque progression/rupture. This will lead to a quantitative assessment tool for early detection of vulnerable plaques and prediction of possible ruptures.Our research project aims to combine medical imaging, computational modelling, phantom experiments and pathological analysis to investigate plaque progression and vulnerability to rupture in both animal models and patients with carotid stenosis.We will identify and …
- 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
Computational methods for multi-scale structural optimisation
Structural optimisation is a powerful computational methodology for finding high-performing designs for structural components or material architectures. For example, what periodic scaffold would provide the highest possible stiffness for its weight?Solving such a problem computationally requires an understanding of the relevant equations required to model the physical properties of interest, as well as efficient implementation of a range of numerical methods including finite elements, finite differences and optimisation.With recent developments in 3D printing technologies it is now becoming possible to …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
Exact and approximate solutions of diffusion on evolving domains
Classical applications of mathematical analysis involve solving partial differential equation models on fixed domains, e.g. 0 < x < L. Applications in biology, however, involve studying diffusive transport on rapidly evolving domains, e.g. 0 < x < L(t), where L(t) represents the length of the evolving tissue. While many problems have been addressed for the case where L(t) increases, less attention has been paid to cases where we consider diffusion on an oscillating domain.In this project we will construct exact …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
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