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 468 matching student topics
Displaying 97–108 of 468 results
Artificial Intelligence for collaborative and intelligent user interfaces
This project seeks to leverage recent advances in machine vision and natural language processing algorithms to support the design and development of knowledge-driven applications that support communication and collaborations with their users.One particular area where this will be investigated is in workplaces for supported employment, that is employment opportunities for people with intellectual disability. One of the questions to address is how machines could respond to what a user shows them in order to assist with decision making in a …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
5G and IoT smart ontology learning
This project aims to investigate privacy preservation protocols in a 5G integrated IoT environment through an analysis of the depth of smart-device use in common smart domains. 5G’s addition to IoT-based smart devices will be effectively deployed and utilised by a large majority of individual and organisation-based users. The knowledge-based ontology and tools developed in the project will help form the new privacy preservation mechanisms that are required for the 5G enabled environment.The development of an ontology for 5G enabled …
- Study level
- PhD, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Citizen-developers: challenges and opportunities for low-code/no-code automation
Robotic Process Automation (RPA) is becoming a popular choice for organisations to support their digital transformation and to maintain operational resilience. Many organisations are keen to adopt Robotic Process Automation (RPA) to dramatically improve operational efficiency. Many organisations train and assign their staff as “citizen-developer” to design, test, and maintain the bots using Low-Code/No-Code platforms. However, there are number of issues surfaced when using organisational employees as citizen developer ranging from technical & process capabilities to scalability of RPA.
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
The dark side of robotic process automation
Pandemics such as COVID 19 have forced organisations to pursue hyper-automation to maintain operational sustainability. Many organisations are keen to adopt Robotic Process Automation (RPA) to dramatically improve operational efficiency. However, evidence to date highlighted various associated challenges associated with adoption of RPA in organisations.Furthermore, recent surveys by consultant organisations found a high RPA project fail rate and their inability to meet the expected return on investment.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Future Enterprise
Alleviating corruption: a data driven perspective
Corruption is cited as among the greatest challenges faced by government and citizenry the world over and threatens to undermine the very trust that is essential for a functioning democratic society. In order to earn and maintain public trust, governments at all levels must continuously strive to reduce corruption and uphold the highest levels of integrity.Amidst the countless human interactions and electronic transactions that occur within the public service on a daily basis are a complex and ever-changing variety of …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Understanding responsible deployment of computer vision for urban planning
Advances in artificial intelligence (AI) offer urban planning practice many novel prospects. By the responsive use of AI, planners can effectively analyse data, improve processes, increase efficiency, and prioritise human-centric aspects of planning to develop sustainable cities. Computer vision is one of the key areas where responsible AI is applied in urban planning to revolutionise the analysis and interpretation of visual data, like images and videos captured in cities to aid decision and plan making processes. While the potential impacts …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
Parameter identifiability for stochastic processes in biological systems
Stochastic models are used in biology to account for inherent randomness in many cellular processes, for example gene regulatory networks. Noise is often thought to obscure information, however, there is an increasing understanding that some randomness contains vitally important information about underlying biological processes.When applying these models to interpret and learn from data, unknown parameters in the model need to be estimated. However, not all data will contribute to a given estimation task regardless of the data quantity and quality. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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
Automating drone traffic management systems
Unmanned Traffic Management (UTM) describes a set of systems, services and procedures that will be developed to manage drone (unmanned aircraft systems/unmanned aerial vehicle/remotely piloted aircraft) operations in and around our cities. From surveillance tasks and package delivery through to passenger transport, UTM will be essentially in ensuring safe and efficient use of our airspace. Essentially, UTM is a new air traffic control system for drones with high levels of automation and advanced decision making and control. This research aims …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Understanding the impacts of biodiversity-focused interventions to agri-food systems on people and nature
Despite efforts to monitor and manage declining species and ecosystems around the world, biodiversity is still not routinely included in mainstream decision-making and continues to decline at the highest rate in human history. Added to this is the problem that both natural and agri-food systems are continually changing due to human and natural disturbances, with climate change likely to increase the impacts of extreme events like drought, fire and economic shocks. Because of large uncertainties and trade-offs between many human …
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Biology and Environmental Science
- Research centre(s)
- Centre for Agriculture and the Bioeconomy
Centre for the Environment
Continuous time samplers (MCMC at the limit!)
The goal of this project is to develop new continuous time Monte Carlo methods for efficient sampling from high-dimensional distributions. Continuous-time Monte Carlo methods are a class of algorithms that use continuous-time dynamics to generate samples from target distributions, rather than the discrete-time dynamics used in traditional Markov chain Monte Carlo (MCMC) methods. These methods have been shown to have faster mixing and better exploration of the state space, making them particularly appealing samplers for challenging distributions.The main objectives of …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Southern ocean aerosols: Sources, sinks and impact on cloud formation and climate
Robust prediction of human-induced global warming and future climate requires more accurate climate models. Currently, aerosol-clouds interactions represent the largest source of uncertainty in our global climate models. To reduce this uncertainty, we need a better understanding of aerosol sources, chemical and physical properties, and processes impacting their growth to sizes where they can act as Cloud Condensation Nuclei (CCN) and interact with incoming solar radiation.The Southern Ocean is a region of the world where climate and weather models, including …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Earth and Atmospheric Sciences
- Research centre(s)
-
Centre for the Environment
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