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 43 matching student topics
Displaying 13–24 of 43 results
Mapping the world: understanding the environment through spatio-temporal implicit representations
Accurately mapping large-scale infrastructure assets (power poles, bridges, buildings, whole suburbs and cities) is still exceptionally challenging for robots.The problem becomes even harder when we ask robots to map structures with intricate geometry or when the appearance or the structure of the environment changes over time, for example due to corrosion or construction activity.The problem difficulty is increased even more when sensor data from a range of different sensors (e.g. lidars and cameras, but also more specialised hardware such as …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
Productive reproducible workflows for deep learning-enabled large-scale industry systems
Deep learning is a mainstream to increase the capability of industry systems, particularly for those with massive data input and output. It is seen that many tools are now claimed to be freely available and could facilitate such process of development and deployment significantly with scalability and quality.However, limited attention has been on developing reproducible and productive workflows to identify the tools and their values towards large-scale industry systems. In this project, we will explore how to design such a …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Computer Science
Systematic evaluation towards the analysis of open-source supply chain on ML4SE tasks
Applying machine learning algorithms to source code related SE task is rapidly developing and attracts the attention from both researchers and industry engineers. While there are many program languages available, applying such techniques, i.e., the representation learning models, for different languages may achieve different performance. Particularly, they all have their own strict syntax, which determines the abstract syntax tree. Thus, a lot of different open-source supply chain are available, for example the parsing tools are used to build AST from …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Computer Science
Fine-grained software vulnerability detection using deep learning techniques
Software vulnerability is a major threat to the security of software systems. Thus, the successful prediction of security vulnerability is one of the most effective attack mitigation solutions. Existing approaches for software vulnerability detection (SVD) can be classified into static and dynamic methods. Powered by AI capabilities, especially with the advancement of machine learning techniques, current software has been produced with more sophisticated methodologies and components. This has made the automatic vulnerability proneness prediction even more challenging. Recent research efforts …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Computer Science
Predicting player performance from one format to another in cricket
Identifying talent as early as possible in elite sport is critical. An important component of this is learning about what metrics of performance in lower grades to focus on to help predict performance in the top grade. This project will explore for this research problem for cricket.
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Data reasoning to extend domain knowledge in deep learning
A wide variety of companies now use personalized prediction models to improve customer satisfaction, for example, detecting cancer relapses, Detecting Attacks in Networks (e.g., SDN) or understanding Customer Online Shopping Behaviour. However, the dramatic increase in size and complexity of newly generated data from various sources is creating a number of challenges for domain experts to make personalized prediction.For example, early detection of cancer can drastically improve the chance and successful treatment. Recently, supervised deep learning has brought breakthroughs in …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
AI-Based Data Analysis on Multiple Imaging Modalities
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. According to the World Health Organization (WHO), it is estimated CVD takes 17.9 million lives every year. In Australian, the statistical data from the Australia Heart Foundation shows CVD is a major cause of death in Australia. It occupies 26% of all deaths, responsible for an average 118 deaths every day. Four of the main types of CVD are coronary heart disease, strokes and transient ischaemic attack, peripheral …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Green polymer-inorganic composite materials
Composite materials are widely researched and widely used in applications such as aircraft, automobiles, ships, structural components and even the space industry.There is a need to create new composite materials which are environmentally friendly and do not use fossil fuel based products. Moreover, the properties of the composites need to be improved while at the same time minimising the costs involved.Consequently our research group is working on composite materials which not only include inexpensive inorganic fillers from the mining sector …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
Machine learning for understanding and predicting behaviour
Understanding behaviour and predicting events is a core machine learning task, and has many applications in areas including computer vision (to detect or prediction actions in video) and signal processing (to detect events in medical signals).While a large body of research exists exploring these tasks, a number of common challenges persist including:capturing variations in how behaviours or events appear across different subjects, such that predictions can be accurately made for previously unseen subjectsmodelling and incorporating long-term relationships, such as previously …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Using machine learning to understand how the world’s microbiomes are changing due to climate
Shotgun metagenomic sequencing has become commonplace when studying microbial communities and their relationship with the health of our planet, and their direct effects on our own health. Currently, there are >180,000 shotgun metagenomes publicly available, but until recently trying to treat these data as a resource has been challenging due to its extreme size (>700 trillion base pairs).Recently we have developed a tool that can efficiently convert this base pair information into a straightforward assessment of which microorganisms are present …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
-
Centre for Microbiome Research
Developing predictive models, methods and analytics for complex sports data
A 3-year strategic partnership on sports data science between the Centre for Data Science (CDS), the Australian Institute of Sport (AIS) and the Queensland Academy of Sport (QAS) was launched in the past few months. With a drive towards data informed decision making across the high performance sports network nationally, a number of collaborative, interdisciplinary research and scholarship opportunities ranging from VRES, to honours, masters and PhD have developed.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Statistical methods for detecting Antarctic ecosystems from space
Satellite images are a frequent and free source of global data which can be used to effectively monitor the environment. We can see how the land is being used, how it’s being changed, what’s there – even where animals are in the landscape. Using these images is essential, particularly for regions where data is expensive to collect or difficult to physically access, like Antarctica. In Antarctica and the sub-Antarctic islands, satellite images can be an easy and quick way to …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Centre for the Environment
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