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 31 matching student topics
Displaying 1–12 of 31 results
Laser light sensors that see through containers
Raman scattering has been used as a powerful 'fingerprinting' technique for more than 80 years, and is widely used by security and law enforcement for detecting hazardous threats. To better safeguard the community, QUT has developed a unique eye -safe laser Raman sensing system for detecting threats, that works at distance from a target (> 10 m) and operates in real time. Increasingly however, threats are being concealed in order to avoid detection. This project will investigate what range of …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
Separating nonlinear optical effects in optical limiters
Optical limiting uses a medium’s nonlinear response to allow light at low intensities to be transmitted, but restricts transmission at high intensities so as to safeguard sensitive detectors including the eye. A popular nonlinear process used in optical limiters is two photon absorption where two high intensity light photons are simultaneously absorbed thereby reducing the light transmission through the medium. Unfortunately, in gold nanoparticle optical limiters a second nonlinear process can arise – saturated absorption which leads to an increase …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Growth and characterisation of epitaxial graphene for electronic and sensing applications
The extraordinary properties of graphene, a single sheet of carbon atoms (e.g. monodimensional structure, high conductivity, low-noise characteristics) are expected to be exploited in the next generation of electronic devices and gas sensors. These applications require a perfect control of the growth of graphene layers, and an optimum integration with the processes and materials used in the semiconductor industry.This project aims at studying the growth of graphene obtained by heating crystalline SiC at high temperature in Ar atrmosphere and ultra …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Two dimensional heterostructures on SiC for new electronics
The present electronic technology is approaching the limit to the smallest circuit element achievable, and the future electronic devices will depend critically on the development of novel approaches. Two dimensional materials seem to offer an exciting perspective, and the advent of graphene (a single layer of carbon atoms in a honeycomb structure) sparked a huge interest, but its application to electronics are limited by the absence of a band gap.A new perspective has been open by other 2D materials which …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Physics-informed diffeomorphic image registration
Medical image registration is the process of finding a spatial transformation that aligns a medical scan or image (X-ray, CT, MR, US, PET etc.) to another scan or image for comparison. Example use of image registration includes mapping one patient's brain MRI onto another's, or tracking organ motion across breathing cycles in lung CT. Accurate registration is a primary requirement of a wide range of clinical workflows, including disease progression monitoring, treatment planning, and atlas-based segmentation.Diffeomorphic registration methods constrain the …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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
Physics-informed reinforcement learning for complex environments, using graph neural networks
Neglecting to incorporate physics information into world models for reinforcement learning leads to reduced adaptability to dynamic and complex environments and overall learning outcomes.In this project, we endeavour to develop and implement learnable models in reinforcement learning (RL) based on graph neural networks (GNNs). These models will integrate object and relation-centric representations to enable accurate predictions, strong generalization, and system identification in complex, dynamical systems. Additionally, we will focus on leveraging extensive world knowledge or physics information to refine representations …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Physics-informed machine learning
Recent advances in computer vision have demonstrated superhuman performance on a variety of visual tasks including image classification, object detection, human pose estimation and human analysis. However, current approaches for achieving these results center around models that purely learn from large-scale datasets with highly complex neural network architectures. Despite the impressive performance, pure data-driven models usually lack robustness, interpretability, and adherence to physical constraints or commonsense reasoning.As in the real world, the visual world of computer vision is governed by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Physics informed machine learning for energy forecasting
Accurate forecasting is at the heart of many modern industries from energy and transport to retail, supply chains, finance, climate, and health. This research project explores deep learning approaches for time-series forecasting, investigating how modern architectures such as recurrent neural networks, LSTMs, Temporal Convolutional Networks, transformers, and multimodal foundation models can shape the next generation of forecasting systems.The overarching goal is to develop robust, interpretable, and scalable forecasting models that outperform classical methods and work effectively in real-world settings, including …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Energy Transition Centre
Modelling the osteoid: A quantitative experimental–mathematical platform for osteocyte network formation and transport
Project Reference: #4MPQC-NATP3Preferred Project Start: Late 2026/Early 2027How does a newly formed bone matrix develop into a mineralised tissue containing a functional osteocyte network, and how does this transformation affect the movement of nutrients and signals?During bone formation, osteoblasts produce a soft, initially unmineralised extracellular matrix known as osteoid. Some osteoblasts gradually become embedded within this matrix and transition into osteocytes, extending dendritic processes that connect them into an intricate three-dimensional network. As the osteoid matures and mineralises, its architecture, …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
A new physics informed machine learning framework for structural optimisation design of the biomedical devices
The machine learning based computer modelling and simulation for engineering and science is a new era. The optimisation analysis is widely used in the design of structures.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
Centre for Biomedical Technologies
Low-cost portable Magnetic Resonance Imaging for clinical applications
The aim of this project is to develop accurate low-cost medical imaging methodology for pseudo-3D mapping of Mammographic Density (MD) within the breast. MD is the degree of radio-opacity (“whiteness”) in an X-ray mammogram. It has implications for breast cancer risk, ease of detection of breast cancer, and monitoring of the efficacy of hormonal breast cancer prevention or anti-cancer treatments.Healthcare ChallengeThere is a growing need for affordable and accurate quantitative assessment of MD without ionising radiation. Magnetic resonance imaging (MRI) …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
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