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 52 matching student topics
Displaying 13–24 of 52 results
Modelling of electrochemical CO2 capture and conversion
Renewable electricity is remarkably cheap, and is only going to get cheaper. However, existing state-of-the-art CO2 capture and conversion processes use thermal energy (typically generated by burning natural gas). This modelling project will investigate electrochemical techniques for capturing CO2 from air (direct air capture) and converting it to useful chemicals and materials.
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
Modelling of sugar cane crushing
Cane sugar factories, while producing sugar and molasses, provide their own energy and power from the sugar cane biomass residue, are green house gas neutral and can export renewable electricity to the grid. The performance of the milling train in extracting juice and dewatering the biomass bagasse residue are key components of the operation. Understanding and modelling the process are seen as a way forward to improve the performance, for example by reducing the final bagasse moisture below the current levels.
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Agriculture and the Bioeconomy
Modelling the response of expansive soil under wetting and drying
Expansive soils are those which can experience significant volume change when water content varies and as of this reason they are considered as problematic soils in geotechnical engineering. Expansive soils are widely distributed globally and cover a significant percentage of world land surface, especially in arid and semi-arid area.In Australia, expansive soil covers around 20% of surface soils and approximately 30% of the total ‘built-up’ land area is covered by expansive soils. This figure is expected to increase, as the …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Materials Science
Evaluating the performance of PODs due to composite load models with high levels of embedded Distributed PVs (D-PV)
Power oscillation Damper (POD) in South QLD are used to provide sufficient damping to inter-area mode of oscillations (electro-mechanical modes). These oscillatory modes often change their characteristics due to changes in load dynamics and the inherent transmission system topology.While the interconnections between generators and transmission lines have not changed over the recent years, there is a significant change in the embedded load dynamics. With high penetration of rooftop PV (including PV distributed at LV and MV level), in South QLD, …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Joint PhD QUT/Münster: Digital innovation in battery energy storage systems
Queensland is a global leader in residential solar photovoltaic adoption, yet battery energy storage uptake is comparatively low, constraining the full potential of decentralised battery energy storage systems (BESS). Similarly, in Germany, battery storage adoption remains limited and regionally concentrated, despite strong national policy support and technological advances in battery manufacturing.This project investigates the behavioural and systemic barriers to BESS adoption and explores how digital solutions can influence energy decisions. It forms part of a broader international collaboration between QUT, …
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
-
Energy Transition Centre
Cyber-security aspects of battery storage systems
Lithium-ion (Li-ion) batteries are a key energy storage component in various electrical and electronic systems such as mobile phones, electric vehicles and grid storage. A properly designed battery management system (BMS) is crucial to guarantee the safety, reliability, and optimal performance of the battery as well as to interconnect the battery systems with each other and external systems through communication channels. However, security threats of the Li-ion battery systems are often overlooked by BMS developers in the design phase. The …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
-
Centre for Clean Energy Technologies and Practices
Developing models of failure for porous materials
Classical fracture mechanics accurately predicts the failure strength of samples with sharp flaws such as pre-existing cracks. However, to predict the failure of porous materials we need to develop an understanding of how stresses are concentrated around smooth flaws in the material such as rounded pores, and how these stress concentrations contribute to failure.Finite fracture mechanics combines the energy criterion for failure from classical fracture mechanics with a stress criterion from macroscopic failure theory. The coupled criterion has by now …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
Data-informed decision-making in education systems
This project explores how advanced statistical modelling can support decision-making in complex education systems. It focuses on modelling uncertainty, interdependencies, and policy-relevant insights to guide planning and practice.The project aligns with research applying Bayesian networks to real world decision-making under uncertainty across infrastructure, health, and education contexts
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Education
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
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Surprising genomes
Genomic sequencing has changed radically since the first public sequencing projects more than 25 years ago. The original human genome project cost more than two billion dollars; sequencing a human genome now costs as little as a thousand, and we may sequence whole viruses and bacteria as a matter of routine.The challenge now lies in rapidly analysing these genomes as they appear, and understanding quickly whether there is anything interesting in the new sequence to warrant further inquiry. This project …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
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
Efficient predictive models using physics-informed machine learning
This research explores how advanced physics-informed neural network models can guide the development of simplified yet accurate predictive systems across scientific and engineering domains. The work spans machine learning, computational physics, and applied mathematics, addressing the critical challenge of creating efficient models that maintain physical consistency and predictive reliability.Recent advances in neural operator learning and physics-informed architectures have demonstrated potential for dramatically reducing model complexity while preserving domain-specific knowledge. This research investigates generalisable frameworks for developing simplified predictive models that …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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