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 57 matching student topics
Displaying 25–36 of 57 results
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
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
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
Equation learning for partial differential equation models of stochastic random walk models
Random walk models are often used to represent the motion of biological cells. These models are convenient because they allow us to capture randomness and variability. However, these approaches can be computationally demanding for large populations.One way to overcome the computational limitation of using random walk models is to take a continuum limit description, which can efficiently provide insight into the underlying transport phenomena.While many continuum limit descriptions for homogeneous random walk models are available, continuum limit descriptions for heterogeneous …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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
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)
-
Centre for Microbiome Research
Re-localisation in natural environments
Re-localisation in robotics involves the process of determining a robot's current pose, consisting of its position and orientation. This can either be within a previously mapped and known environment (i.e. prior map) or relative to another robot in a multi-agent setup. Re-localisation is essential for enabling robots to perform tasks such as autonomous monitoring and exploration seamlessly, even when they encounter temporary challenges in precisely tracking their location in GPS-degraded environments. For instance, consider the 'wake-up' problem, where a robot …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Enhancing 3D visual understanding through multimodal data fusion
The demand for 3D scene understanding through point clouds is rapidly growing in diverse applications, including augmented and virtual reality, autonomous driving, robotics, and environment monitoring. However, the field faces challenges due to limited data availability and predefined categories. Training deep 3D networks effectively for sparse LiDAR point clouds requires significant amounts of annotated data, which is both time-consuming and expensive. Building on the advancements in 2D models that leverage the power of image and language knowledge, our project aims …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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 Data 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
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
Contact us
If you have questions about the best options for you, the application process, your research topic, finding a supervisor or anything else, get in touch with us today.