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 21 matching student topics
Displaying 13–21 of 21 results
Unlocking the Potential of Simplex-Truncated Distributions
This PhD project aims to develop new methods for generating random samples from a specific type of probability distributions called simplex-truncated distributions. These distributions are commonly used in various fields such as statistics, machine learning, and biology.The project will involve the development of new techniques to generate random samples from simplex-truncated distributions. These techniques are based on a method called continuous-time Monte Carlo which is a cutting edge method in statistics that can generate random samples from complex distributions.The main …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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
Modelling and managing uncertain Antarctic species networks
Antarctic ecosystems are complex, and data is limited since it is expensive to collect. Species including penguins, seabirds, invertebrates, mosses, and marine species interact in food webs which can be modelled as mathematical networks. These networks can be large, span across terrestrial and marine systems, and are changing in response to environmental changes.These ecological networks can be modelled using differential equation predator prey models like Lotka-Volterra to describe these interactions. However, the relationships between species are not always known, or …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Centre for the Environment
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
Parameter identifiability for stochastic processes in biological systems
Stochastic models are used in biology to account for inherent randomness in many cellular processes, for example gene regulatory networks. Noise is often thought to obscure information, however, there is an increasing understanding that some randomness contains vitally important information about underlying biological processes.When applying these models to interpret and learn from data, unknown parameters in the model need to be estimated. However, not all data will contribute to a given estimation task regardless of the data quantity and quality. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Computational drug repurposing for neuropsychiatric disease
While hundreds of robust genetic associations have been found for neuropsychiatric disease (such as schizophrenia, major depression, and anxiety) understanding the exact molecular mechanisms leading to disease onset and progression remains challenging. Inherited (i.e. genetic) risk factors for many neuropsychiatric diseases converge on genes that are co-ordinately expressed (co-expressed) in a disease-relevant tissue (e.g. brain). The study of how genetic risk factors affect co-expressed genes (i.e. gene co-expression analysis) has the potential to uncover new biological processes underlying disease onset. …
- Study level
- Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
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
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
Maxwell's Demon revisited: Molecular simulations as a statistical physics learning tool
In his 1871 'Theory of Heat', James Clerk Maxwell introduced a fictitious being who can violate the second law of thermodynamics by following the trajectory of every molecule within a gas.The being, later dubbed 'Maxwell's Demon' by Lord Kelvin, would operate a small trapdoor in a partitioned container to allow hotter and colder molecules of the gas to pass to opposite sides of the container. The Demon would be able to raise the temperature of the gas in one half …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
Contact us
If you have questions about the best options for you, the application process, your research topic, finding a supervisor or anything else, get in touch with us today.