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 241 matching student topics
Displaying 73–84 of 241 results
Assessing coral rubble restoration on the Great Barrier Reef
Coral reefs face cumulative threats from climate change to shipping and the concern is that this can cause reefs to transition from coral to rubble dominated states. The formation of coral rubble is a natural part of the reef cycle, however, too much rubble can decrease the resilience of reefs and prevent recovery. A number of coral rubble stabilisation methods are being utilised globally including Mars Assisted Reef Restoration System of hexagonal metal units that are deployed on reefs with …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Earth and Atmospheric Sciences
- Research centre(s)
-
Centre for the 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
Anode-free Batteries
The lithium-metal battery (LMB) has been regarded as the most promising and viable future high-energy-density rechargeable battery technology due to the employment of the Li-metal anode. However, it suffers from poor energy density and safety, and improved battery design is sought. The anode-free full-cell architecture is constructed from a fully lithiated cathode with a bare anode Cu current collector. In such an anode-free lithium battery, both the gravimetric and volumetric energy densities can be extended to the maximum limit. Moreover, …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Phosphate-based polyanionic cathode materials for (post) Li-ion batteries
Mixed polyanionic compounds have been studied extensively as viable cathode materials for sodium-ion batteries. Mixed phosphates Na4M3(PO4)2P2O7 (M = Mn2+, Fe2+, Co2+, Ni2+), provide a low barrier for Na-ion diffusion, being advantageous in comparison to phosphates and pyrophosphates. Despite being structurally similar, electrochemical performance differs for their analogues with different degrees of (de)sodiation, according to the transition element present. This project will develop series of mixed phosphates using novel rapid heating methods to achieve desired electrochemical properties.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Mathematical tools for stochastic and continuum transport models
Mathematical models of particle transport are fundamental to many applied disciplines including physics, biology, ecology and medicine. Particle transport is typically modelled using either a stochastic model, where probability rules govern the motion of individual particles, or a continuum model, where partial differential equations govern the concentration of particles in space and time. This project aims to use analytical and numerical techniques from applied and computational mathematics to address one or both of the following questions:what is the average time …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
Statistics via scalable Monte Carlo
Monte Carlo methods use random sampling to approximate solutions to challenging problems. These methods are helpful for statistical models with many parameters, as discussed in this short video. The methods are particularly useful for Bayesian inference where one wishes to get a rigorous understanding of parameter uncertainty.Despite having many advantages over their competitors, Monte Carlo methods can be very slow in the context of big data. In this project, you'll help develop scalable Monte Carlo methods to enable timely and …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Strengthening security for cloud computing applications
In today's digital landscape, applications are increasingly being deployed on cloud platforms, offering benefits such as streamlined management and cost-effectiveness. However, even with the efforts of cloud providers to deliver reliable services, the risk of runtime failures and faults still exists. This project aims to address this challenge by exploring innovative approaches to detect and mitigate errors that occur during the operation of cloud-based applications. By proactively identifying and resolving runtime issues, we can enhance the overall performance, reliability, and …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Computer 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
Facilitating Towards Sustainable Electric Vehicles Trading System
The number of Electric Vehicles (EVs) on the road is expected to reach 145 million by 2030. As the number of EVs on the road increases, the demand for energy to charge these vehicles also grows. Traditional charging infrastructure may not meet the increasing energy demands, which may lead to increased waiting time in those charging stations. The EV-EV trading scheme is a promising solution that allows EV owners to access additional energy from nearby EVs. This scheme has attracted …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Real-time Business Process Integration in the Industrial Internet of Things (IIoT) for Industry 4.0
The vision of Industry 4.0 is to support business capabilities at the edge. The Industrial Internet of Things (IIoT) enables this vision by integrating IoT with Enterprise Systems (ESs). In an IIoT process, sensor applications at the edge require seamless integration with the software services of ESs. This, in turn, facilitates the real-time correlation of sensor events with BPs. However, existing IIoT architectures lack the necessary architectural capabilities to reflect the true essence of Industry 4.0.This research aims to develop …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Efficient Parameter Estimation for Stochastic Simulations
Stochastic simulation-based models are routinely used in many areas of science to describe inherent randomness in many real-world systems. Applications include the study of particle physics, imaging if black holes, biochemical processes, the migration of animals, and the spread of infectious diseases. To apply these models to interpret data requires statistical methods to estimate model parameters.Unfortunately, standard statistical techniques are not capable of analysing data using these models. This is largely due to the model likelihood, the probability of the …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Enabling effective novice-expert interactions: co-designing immersive systems for complex remote data analysis
This student research topic focuses on exploring and developing immersive systems that facilitate interactions between novice and expert users in the context of complex remote data analysis. The goal is to design and prototype innovative solutions that enhance the collaboration, learning, and training aspects of data analysis tasks.The research will involve investigating various immersive technologies and their potential application in bridging the gap between novice and expert users, enabling effective knowledge transfer and skill acquisition.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
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