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 105 matching student topics
Displaying 49–60 of 105 results
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, Vacation research experience scheme
- 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, Vacation research experience scheme
- 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, Vacation research experience scheme
- 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
Novel shoulder musculoskeletal modelling to simulate pathological cases
The project will focus on adapting and using a novel musculoskeletal model of the shoulder joint to simulate pathological cases.
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
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, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Advancing Water Quality Monitoring in Queensland through Data Science
Organisations throughout Queensland are actively involved in the real-time monitoring of water quality parameters in rivers that contribute to the health of the Great Barrier Reef. This crucial assessment is conducted using in-situ sensors and grab samples to measure key parameters such as nitrate and turbidity. However, it is important to note that while sensor data is collected at high frequencies, they are often affected by anomalies leading to potential errors. Consequently, physical visits to monitoring sites are required to …
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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
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, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Next-generation traffic signals for Safe, Efficient and Green Intersections
There is a full PhD scholarship available in the School of Civil and Environmental Engineering at Queensland University of Technology (QUT) to support the newly awarded ARC Linkage Project on Next-generation traffic signals using artificial intelligence-based video analytics for safe, efficient and green intersections. The stipend has a cash value of $32,500 per annum for 3 years.To apply for this position, please submit the following documents via email to m1.haque@qut.edu.au:a detailed curriculum vitae (CV) highlighting academic achievements, research experience and …
- Study level
- PhD
- School
- School of Civil and Environmental Engineering
Sustainable energy transition with system dynamics
The challenge to keep global warming to 1.5°C above pre-industrial levels has become even greater due to a continued increase in greenhouse gas emissions (IPCC, 2023). One major challenge is the shift from fossil fuels to renewable energy to reduce emissions (Gholami et al., 2016). The share of renewable energy in electricity generation has increased to 28.3%, however, an acceleration of the pace of the transition is required to limit global temperature rise (REN21, 2022).New energy policies are needed to …
- Study level
- PhD, Master of Philosophy, Honours, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Information Systems
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
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