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 58 matching student topics
Displaying 25–36 of 58 results
Visualisation and sonification for genomic data sets
Successive revolutions in sequencing technology over the past two decades have led to an explosion in the availability of genomic data. Analysing biological datasets and identifying relationships within them is challenging - some of the process can be automated but interactive exploration offers a number of advantages, and supports serendipitous discovery.This project looks at visual analytics and sonification - the use of sound and musical encodings - to enhance our understanding of biological networks.
- Study level
- PhD, Master of Philosophy, Honours, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Optimising a soft robot using a differentiable physics simulator
A physics simulator is a valuable tool for designing robots. For example, it helps in optimising geometry, choosing appropriate materials, predicting dynamics, and improving control algorithms without having to build a real robot. This allows designers to rapidly test multiple designs before fabricating the robot.Soft robotics is the field that deals with the design of robots using materials like rubbers and plastics that readily deform when a load is applied. This gives a lot more freedom in designing robots that …
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
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, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Computer modelling of water infiltration into soils
This project involves investigations of the process of water infiltration into soils using the computational modeling method (numerical simulation). This problem is very important in agriculture, where an understanding of how water is distributed within the soil after rainfall is essential.
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
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, Vacation research experience scheme
- 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, 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
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
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
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
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