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 87 matching student topics
Displaying 37–48 of 87 results
Artificial intelligence (AI) to balance fluctuations of intermittent renewable energy sources
Artificial intelligence (AI) can play a significant role in analyzing and predicting energy consumption and production patterns from renewable sources such as solar and wind (Lyu & Liu 2021). This is particularly important due to the key challenge of intermittency, where major renewable sources for electricity, such as solar and wind, are subject to the inconsistencies of the weather (Watson et al., 2022).In this project, we investigate how AI and machine learning algorithms can optimize smart grids and other components …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Future Enterprise
Energy Transition Centre
From a descriptive to a predictive understanding of the human microbiome
Microorganisms have a profound influence on biological, environmental, and industrial processes, but understanding the complex dynamics of microbial communities and how to manipulate them to our advantage remains a challenge. CMR Director Professor Gene Tyson has recently been awarded a prestigious ARC Laureate Fellowship that aims to overcome current technological limitations and transform microbial ecology from a descriptive to a predictive science. This will be achieved using as a model the most intensively studied ecosystem on the planet: the human …
- Study level
- PhD
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
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Centre for Microbiome Research
Driver engagement and risk in automated driving: Advanced data analytics leveraging driver monitoring systems
The project aims to the explore concept of empathic machines in the context of driver monitoring systems (DMS) and automated driving. The successful candidate will contribute to advancing the understanding of driver engagement, situation awareness, and risk through leveraging advancements in data science techniques on vehicle sensor, DMS, and other related datasets.To apply for this position, please submit the following documents:a cover letter outlining your research interests, relevant qualifications, and motivation to join the Empathic Machines projecta detailed curriculum vitae …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Data Science
Centre for Future Mobility
Assessing the quality of cluster analysis
Machine learning cluster methods are common classification methods, but methods for assessing performance are limited as are methods for explaining how they work. Exploring methods for both assessing and explaining performance are the subject of this research with application to real-world contexts with the Australian Bureau of Statistics.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Machine Learning-based pedictive tool for energy storage
The fundamental idea behind the ML approach is to analyze and map the relationships between the physical,chemical, and energy storage properties of materials with their associated output data. This early understanding of the energy storage capabilities through the ML approach helps the material scientists to clearly understand, discover, and optimize the fabrication process to develop highly efficient energy storage systems. It also provides key steps in the device fabrication process omitting excessive experimental stages.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Cybersecurity for open-source software using machine learning and AI
People are increasingly using open-source software in businesses and industries. These software programs are made by a community of developers and are managed by platforms like PyPI and npm. However, there is a worry about the safety of these programs because hackers add harmful code to compromise security and steal important data. This project explores approaches to detect harmful open-source projects using machine learning and AI.
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
Exploring green infrastructure optimisation for climate change adaptation and mitigation
Green infrastructure refers to public and private green spaces in cities that provide water cycle benefits. These green spaces range in the range from single trees on city streets to urban parks, and waterway walkways. Some are natural, such as the remains of native plants, while others are more geometric, for example green roofs and green walls. Green infrastructure can increase the sustainability and vitality of cities through benefits such as greening and cooling, water quality, and managing hotter weather. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
The Impact of AI on Leadership Roles and Structures
Examine how the introduction of AI technologies reshapes traditional leadership roles and organisational structures. Investigate the evolving nature of leadership in decentralised, AI-driven decision-making processes and explore how leaders can effectively adapt to new leadership paradigms.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
-
Centre for Behavioural Economics, Society and Technology
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
Identifying Indigenous contributions to knowledge
The Australian Census collects data every ten years to reflect who we are as a nation. But the data collected by the Census only tells part of our story.Indigenous people lived in Australia for thousands of years before the arrival of European settlers, accumulating a wealth of knowledge about Australia's land, climate, flora and fauna. Researchers have only begun tapping this knowledge as the basis for modern scientific research.This project will combine machine learning and text-analytics tools to develop a …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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
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