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 488 matching student topics
Displaying 25–36 of 488 results
Multi-modal sentiment analysis
In deep learning models, language models and word embedding methods have become popular to understand the context of text data. Popular language models such as BERT have limitations in terms of the token length. There exist some corpora that have longer text with an average of 1000 tokens. Additionally, these corpora are text-heavy and only include some images.In our prior works, we have developed several multi-modality models on social media datasets.
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Evaluation of language models and word embedding methods for natural language processing applications
In deep learning models, language models and word embedding methods have become popular to understand the context of text data. There exist many variants of these methods and have different limitations. This project will introduce you to the hot topic of language models and the fields of Natural Language Processing and Text Mining.
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Curvature dependence of reaction-diffusion wave front speed with nonlinear diffusion.
Reaction-diffusion waves describe the progression in space of wildfires, species invasions, epidemic spread, and biological tissue growth. When diffusion is linear, these waves are known to advance at a rate that strongly depends on the curvature of the wave fronts. How nonlinear diffusion affects the curvature dependence of the progression rate of these wavefronts remains unknown.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
Optimising bone shape with memory networks
Bone is a dynamic tissue that optimises its shape to the mechanical loads that it carries. Bone mass is accrued where loads are high, and reduced where loads are low. This adaptation of bone tissue to mechanical loads is well-known and observed in many instances. However, what serves as a reference mechanical state in this shape optimisation remains largely unknown.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Biomedical Technologies
Automatic Generation of Software Vulnerability Datasets for Machine Learning
In recent years, machine learning has enjoyed profound success in a range of interesting applications such as natural language processing, computer vision and speech recognition. It has been possible mainly due to, in addition to better computing resources, the availability of large amounts of training datasets to these applications. However, in software security research, the lack of large datasets is an open problem that makes it challenging for machine learning to reason about security vulnerabilities found in real-world software. The …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Computer Science
Building explainable and trustworthy intelligent systems
Existing machine learning-based intelligent systems are autonomous and opaque (often considered “black-box” systems), which has led to the lack of trust in AI adoption and, consequently, the gap between machine and human being.In 2018, the European Parliament adopted the General Data Protection Regulation (GDPR), which introduces a right of explanation for all human individuals to obtain “meaningful explanations of the logic involved” when a decision is made by automated systems. To this end, it is a compliance that an intelligent …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Surprising genomes
Genomic sequencing has changed radically since the first public sequencing projects more than 25 years ago. The original human genome project cost more than two billion dollars; sequencing a human genome now costs as little as a thousand, and we may sequence whole viruses and bacteria as a matter of routine.The challenge now lies in rapidly analysing these genomes as they appear, and understanding quickly whether there is anything interesting in the new sequence to warrant further inquiry. This project …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Habitable water infrastructures
This project explores buildings, public/civic spaces, and landscapes as water infrastructure. Water is integral to human survival; hence understanding buildings and urban spaces as habitable water infrastructure has the potential to mitigate the effects of the climate crisis and navigate too much water (floods) and too little water (drought) while offering different modes of occupation.With increasing rainfall intensities, floods, rising sea levels, and drought, the pervasive dichotomy between habitable spaces and water infrastructures can no longer hold. The two can't …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
Capturing the impact of patient variability in a novel cancer treatment
In 2015, the Food and Drug Association (FDA) approved a lab-engineered virus for the treatment of melanoma (skin cancer). Since then, there has been a significant increase in the number of lab-grown viruses that are being tested in clinical trials as potential treatments of cancer. Unfortunately, it seems that a large number of patients in these clinical trials fail under this treatment and currently there is no way to distinguish between responders and non-responders to treatment.Fortunately, we can use mathematics …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Advanced materials for redox flow batteries
Grid-scale energy storage for intermittent renewables like solar and wind is an essential element of the transition away from fossil fuel based electricity production. Redox flow batteries have some very interesting characteristics for this stationary storage application:they are safer than other battery typesthe amount of energy stored can typically be scaled up easilythe power and energy of a system are more decoupled compared to lithium and other batteries, making them flexible in their design parameters.Ion exchange membrane and electrode are …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
Robust feature selection and correspondence for visual control of robots
Stable correspondence-free image-based visual servoing is a challenging and important problem.In classical image-based visual controllers, explicit feature correspondence (matching) to some desired arrangement (configuration) is required before a control input is obtained. Instead, this project will investigate variable feature correspondence and robust feature selection to simultaneously solve visual servoing problem, removing any feature tracking requirement or additional image processing.Also involving Prof Jason Ford.Example of recent past work
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Semantic SLAM for robotic scene understanding, geometric-semantic representations for infrastructure monitoring and maintenance
Making a robot understand what it sees is one of the most fascinating goals in our current research. To this end, we develop novel methods for Semantic Mapping and Semantic SLAM by combining object detection with simultaneous localisation and mapping (SLAM) techniques.We work on novel approaches to SLAM that create semantically meaningful maps by combining geometric and semantic information. Such semantically enriched maps will help robots understand our complex world and will ultimately increase the range and sophistication of interactions …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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