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 631 matching student topics
Displaying 265–276 of 631 results
X-ray backscatter diffraction and X-ray fluorescence for the rapid structural and chemical characterisation of rock samples
The advisory team devised a synchrotron-based microscopy method for coeval measurements of elemental concentrations and crystal-orientation data combining X-ray fluorescence microscopy (XFM) with X-ray backscatter diffraction mapping (XBDM).We seek students with a background in physics, crystallography, or mineralogy and coding skills to help with the improvement of the methods.XBDM/XFM has the potential to become a game changer in the microphysical and -chemical characterisation of crystalline solids, with future applications in the critical-minerals industry and material science.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Earth and Atmospheric Sciences
Profiling aerosol liquid water content over Australia
Aerosol liquid water content (ALWC) is a ubiquitous constituent in atmospheric aerosol particles. The degree of ALWC present in aerosol particles is influenced various factors, including relative humidity, temperature, particle mass, size distribution, and aerosol composition. Comprehensive analyses on ALWC have been conducted in the Northern Hemisphere, but similar work has rarely been done in the Southern Hemisphere due to the scarcity of aerosol particle measurements. In the atmosphere, ALWC scatters radiation and reduces visibility, significantly affecting air quality, weather, …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Earth and Atmospheric Sciences
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
Hospital readmission prediction with domain knowledge
The Australian Commission on Safety and Quality in Health Care has highlighted that reducing avoidable hospital readmissions supports better health outcomes, improves patient safety and leads to greater efficiency in the health system. Previous studies have reported that up to 11% of the emergency (ED) population are 'heavy users' with a higher prevalence of psychosocial problems and often co-existing chronic medical conditions. All Australian governments have committed to reforms under the National Health Reform Agreement Addendum,1 and the ability to …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Interpretable software vulnerability detection using deep learning techniques
Software vulnerabilities have been considered as significant reliability threats to the general public, especially critical infrastructures. Many approaches have been proposed to detect vulnerabilities in source code to avoid any damages they pose when exploited. Conventional approaches include static analysis and dynamic analysis. Static analysis uses pre-defined patterns or vulnerability dataset to scan and examine software source code to identify potential vulnerable code snippets. These patterns are manually crafted or identified by software developers or security experts, which are time-consuming. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Quantum machine learning
Quantum machine learning is the integration of quantum algorithms within machine learning programs with great potential to solve complex problems. For instance, Google’s Sycamore processor (61) performs in 200 seconds a task that would require 10,000 years using a classical computer.
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Studying the small proteins of the global microbiome
As part of an ARC Future Fellowship project awarded to Luis Pedro Coelho, we aim to study small proteins with the aim of better understanding them and laying the groundwork for exploiting them for biotechnological purposes. Small proteins (up to 100 amino acids, but often much shorter) have vital roles in all areas of life, but have been neglected in research due to lack of methods.Particular projects in this topic include developing methods for determining function based on genomic context, …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
-
Centre for Microbiome Research
Alignments In Process Mining and Social Sequence Analysis
In process mining, we perform computational analyses of sequential data in order to help organisations improve, in settings from ride-sharing platforms to government departments. In social sequence analysis, we perform computational analysis on sequential data to understand small or large structures in society, such as the progress of careers of 18th century German musicians, or the progress of nations through different stages of economic development.In both process mining and social sequence analysis, calculation of "alignments" for is a key technique …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Process Mining Infrastructure In Haskell
In process mining, sequence analysis algorithms are used to discover computational models of process data, and to analyse them. The insights from these models and analysis then improve the processes in organisations in many real-life domains - from manufacturing, to government, to healthcare. Haskell is a powerful functional programming language well suited to problems involving formal reasoning and pattern matching. This project would advance process mining research by building high-quality, high performance libraries in Haskell for fundamental process mining activities …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Advanced numerical modelling to study fluid flow and heat transfer of ground-mounted photovoltaic panels for the power generation industry
The increase in global energy demand necessitates further advancement in photovoltaic (PV) systems. Advancements in PVs could potentially play a role to help meet the Paris Agreement of limiting global temperature increase to below 2°C.The performance of ground-mounted PV panels commonly found in solar farms depends on a myriad of factors such as tilt angle, microclimate i.e. wind loads, shading, solar irradiance, and dust deposition. This project aims to develop an advanced numerical model, namely computational fluid dynamics (CFD), backed …
- Study level
- PhD, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
Improving language outcomes in people with epilepsy
Epilepsy is a serious and debilitating condition which is grossly under-researched despite the devastating impact it can have. Damage to the vast language processing network of the brain during surgical resection can cause aphasia, a devastating communication disability. This project aims to determine reliable pre-surgical mapping and outcome predictors in epilepsy resection: To 1) develop a reliable and comprehensive battery to map the language network in pre-surgical epilepsy patients with different foci, and 2) assess how the reorganisation of the …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Clinical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
Characterizing effects of radiation therapy in 3D bioengineered cancer models
Radiation therapy (RT) is one of the most commonly used modalities in cancer treatment, usually delivered in combination with surgical intervention, chemotherapy, and immunotherapy.However, clinical outcomes show that almost 20% of patients fail to achieve targeted outcomes because of inherent resistance to radiation. This necessitates in-depth understanding of radiation resistance mechanisms using relevant preclinical models of RT. Previous in vitro studies have predominantly used two-dimensional (2D) cell culture models that do not recapitulate the three-dimensional (3D) complexity of native tissues.
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
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