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 101 matching student topics
Displaying 49–60 of 101 results
Efficient Parameter Estimation for Stochastic Simulations
Stochastic simulation-based models are routinely used in many areas of science to describe inherent randomness in many real-world systems. Applications include the study of particle physics, imaging if black holes, biochemical processes, the migration of animals, and the spread of infectious diseases. To apply these models to interpret data requires statistical methods to estimate model parameters.Unfortunately, standard statistical techniques are not capable of analysing data using these models. This is largely due to the model likelihood, the probability of the …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Efficient parameter estimation for agent-based models of tumour growth
Cancer is an extremely heterogeneous disease, particularly at the cellular level. Cells within a single cancerous tumour undergo vastly different rates of proliferation based on their location and specific genetic mutations. Capturing this stochasticity in cell behaviour and its effect on tumour growth is challenging with a deterministic system, e.g. ordinary differential equations, however, is possible with an agent-based model (ABM). In an ABM, cells are modelled as individual agents that have a probability of proliferation and movement in each …
- 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
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
Engineering bioartificial extracellular tumour microenvironments for Osteosarcoma personalised precision oncology
Osteosarcoma (OS) is the most common malignant bone tumour affecting children and adolescents. Importantly, clinical outcomes have not improved for decades, and bone tumours remain to be a leading cause of cancer-related death in adolescents.By identifying ideal treatment approaches for each individual patient, precision oncology has the potential to significantly improve these outcomes. Yet, its widespread application is hindered by a lack of biomaterials that support the reproducible and robust generation of patient-derived osteosarcoma organoids in vitro.Therefore, this project will …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
- 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
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
Restoring adiponectin signalling to prevent prostate cancer progression
Advanced prostate cancer (PCa) is a leading cause of cancer-associated death in Australian men. Anti-androgens, which exploit the tumour’s reliance on androgens for its growth and spread, offer temporary remission in advanced PCa patients, but due to treatment resistance, fail to be curative. A further complication of anti-androgens is that they trigger a deleterious suite of metabolic side-effects resembling obesity/Metabolic syndrome. These symptoms not only impact patient health but promote the tumour to be more aggressive and resist treatment. Vital …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
Targeting leptin's signalling axis to prevent treatment resistance in prostate cancer
Advanced prostate cancer (PCa) is a leading cause of cancer-associated death in Australian men. Anti-androgens, which exploit the tumour’s reliance on androgens for its growth & spread, offer temporary remission in advanced PCa patients, but due to treatment resistance, fail to be curative. A further complication of anti-androgens is that they trigger a deleterious suite of metabolic side-effects resembling obesity/Metabolic syndrome. These symptoms not only impact patient health but promote tumours to be more aggressive & resist treatment. Vital new …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
Proactive micromobility safety assessment using AI-based video analytics and traffic conflict techniques
A fully funded PhD scholarship is available in the School of Civil and Environmental Engineering at Queensland University of Technology (QUT) as part of a newly awarded Australian Research Council (ARC) Discovery Project titled 'Shaping net-zero cities with safe and efficient micromobility solutions'.This PhD project will investigate the behavioural and safety interactions between pedestrians, micromobility users (e.g. e-scooters and e-bikes), and other road users in shared urban environments. The research will combine AI-based video analytics, trajectory analysis, behavioural modelling, and …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
Regenerating bone following osteosarcoma tumour resection in a post-chemotherapy treated bone defect
Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents. Standard treatment involves surgical resection of the tumour combined with systemic chemotherapy. While most patients undergo limb-sparing surgery to avoid amputation, this often results in significant morbidity and lifelong complications. These complications stem from the creation of large bone defects, poor healing outcomes, the need for revision surgeries, and long-term prosthetic failureThere is a critical clinical need for regenerative strategies that restore bone integrity and function following …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
Hierarchical forecasting: forecasting a collection of time series
Hierarchical forecasting is a method used to generate forecasts at multiple levels of aggregation within a structured hierarchy. This technique is particularly valuable in situations where data can be organised into a hierarchy based on different dimensions, such as geography, product categories, or time. The approach ensures that forecasts at the top levels (e.g. total sales) align with forecasts at the lower levels (e.g. regional or product-level sales), creating a coherent and consistent forecasting process across the entire hierarchy.In many …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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