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 553 matching student topics
Displaying 193–204 of 553 results
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
Novel algorithms for microbiome data
Metagenomics data is complex, high-volume data and keeps evolving, requiring novel computational method development as the wetlab approaches changes and databases grow. Thus, novel computational methods are required to take advantage of them.There are several potential projects under this topic, including:using deep learning to improve metagenomics assemblydeveloping better tools to analyse the presence of resistance genes in metagenomics datadeveloping approaches for estimating the quality of genomes from novel generation sequencespredicting the function of small sequences using more than just sequence.Interested …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
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Centre for Microbiome Research
Australian experiences of algorithmic culture on TikTok
Join a world-leading research team examining how recommender systems are shaping personalised and shared experiences of algorithmic culture in Australia. The project is focused on TikTok and engages with both professional TikTok creators and users using innovative computational and traditional research approaches.The empirical work is structured into three streams:In the Platform Stream we observe the type of content TikTok recommends on the least-personalised version of the platform, to create a close-to-generic baseline of the Australian experience of algorithmic culture on …
- Study level
- PhD
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Communication
- Research centre(s)
- Digital Media Research Centre
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
Development of a machine learning algorithm for high throughput cell response data in drug therapy
High-throughput screening assays are essential for accelerating drug discovery, but current assays often rely on endpoint measurements that do not capture the dynamic response of cells to drug treatment. Machine learning algorithms (MLAs) have the potential to enable real-time, high-throughput monitoring of cell response to drug treatment by analyzing complex datasets generated by multiplexed live-cell assays. This research project aims to develop an MLA for enabling high throughput cell response data in drug treatment. The project will involve three main …
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Computer Science
- Research centre(s)
- Centre for Biomedical Technologies
Centre for Biomedical Technologies
Leveraging Big Data and AI/ML for Smart Transport Solutions
This PhD position aims to harness the potential of big traffic and mobility data alongside cutting-edge AI/ML algorithms to pioneer innovative solutions for optimizing smart motorways and/or arterial traffic flow. By leveraging these technologies, the project endeavours to develop and test smart algorithms, with the goal of significantly enhancing the efficiency and safety of road networks.Send via email to Prof. Ashish Bhaskar (ashish.bhaskar@qut.edu.au):a brief statement detailing your suitability for the positiona detailed curriculum vitae, including a list of publications, if …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Data Science
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