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 442 matching student topics
Displaying 25–36 of 442 results
Human robotic interaction prototyping toolkit
Design relies on prototyping methods to help envisage future design concepts and elicit feedback from potential users. A key challenge the design of human-robot interaction (HRI) with collaborative robots is the current lack of prototyping tools, techniques, and materials. Without good prototyping tools, it is difficult to move beyond existing solutions and develop new ways of interacting with robots that make them more accessible and easier for people to use.This project will develop a robot collaboration prototyping toolkit that combines …
- Study level
- PhD
- Faculty
- Faculty of Creative Industries, Education and Social Justice
- School
- School of Design
- Research centre(s)
-
Design Lab
High-speed robotic waste separation
Sorting waste or recyclables is an important but unpleasant job, currently done by specialised machinery and humans for the hard bits. What are the core challenges that could be done by "robots that see". This is a challenging problem in perception, dynamic path planning and control.
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Implicit representations for place recognition and robot localisation
This project will develop a novel localization pipeline based on implicit map representations. Unlike traditional approaches that use explicit representations like point clouds or voxel grids, the map in our project is represented implicitly in the weights of neural networks such as Neural Radiance Fields (NeRF). You will get a chance to develop a new class of localization algorithms that work directly on the implicit representation, bypassing the costly rendering step from implicit to explicit representation. The designed algorithms will …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Reversing Epithelial Mesenchymal Plasticity with Eribulin to Enhance Therapy Response
Epithelial mesenchymal plasticity (EMP) is a highly regulated and powerful cellular process that is fundamental in embryonic development (1), which is hijacked by cancer cells for metastatic progression and therapy resistance in epithelial cancers (2). Eribulin is a microtubule-inhibiting cancer drug discovered in sea sponges and approved for 3rd line therapy in metastatic breast cancer, which was shown to reverse epithelial mesenchymal transition (EMT) (3).We hypothesise that eribulin’s reversal of EMT will sensitise breast cancer cells to other therapies and …
- Study level
- Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
UAV navigation in GPS denied environments
This PhD project aims to develop a framework for unmanned aerial vehicles (UAV), which optimally balances localisation, mapping and other objectives in order to solve sequential decision tasks under map and pose uncertainty. This project expects to generate new knowledge in UAV navigation using an innovative approach by combining simultaneous localisation and mapping algorithms with partially observable markov decision processes. The project’s expected outcomes will enable UAVs to solve multiple objectives under map and pose uncertainty in GPS-denied environments. This …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Evidence-driven policy innovation for urban heat islands
Extreme heatwaves and other extreme weather events are contributing to the fragility of cities and urban infrastructure, which requires urgent attention. Urban heat islands are an exemplar for metropolitan fragile areas, which exacerbate the impact of climate change and global warming on natural hazards, such as wildfires, storms, floods, and droughts, which pose a critical threat to Australian and international communities (Degirmenci et al., 2021). Decision support systems (DSS) can help city planners and policymakers to optimise their decision-making by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Business and Law
- School
- School of Management
- Research centre(s)
- Centre for Future Enterprise
SleepBeta: co-designing technology with young adults to promote healthy sleep
The aim of the SleepBeta project is collaborate with young adults to promote healthy sleep. Sleep, together with healthy diet and exercise, is a key pillar for a healthy lifestyle. It is important to feeling well and to performing well at school and in university. However, young adults often have unhealthy sleep habits due to stress caused by exams, leisure activities and work commitments, and digital technologies used at night-time. Over the last few years, we explored different sleep and …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
Identifying corporate tax avoidance
It is not possible to empirically measure, with certainty, a corporation’s level of tax avoidance due to a lack of publicly available information. As such, academic studies that seek to identify determinants, moderators and consequences of corporate tax avoidance, in order to evaluate the equity of the tax system (Callihan, 1994), measure corporate tax avoidance by proxy suggesting a wide variety of calculations.But these calculations have limitations. For example, most proxies measure non conforming (transactions that are accounted for differently …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Business and Law
- School
- School of Accountancy
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
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
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
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
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