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 68 matching student topics
Displaying 13–24 of 68 results
Combining solar and vibration energy harvesting for rainfall prediction
Rainfall prediction plays a crucial role in various sectors such as agriculture, water resource management, and disaster preparedness. Traditional prediction methods often rely on complex meteorological models and expensive equipment. However, advancements in energy harvesting technology offer the opportunity to develop low-cost and sustainable solutions for rainfall prediction.This project proposes to leverage solar and vibration energy harvesting for rainfall prediction. Combined measurements from both solar and vibration energy harvesting can provide comprehensive data for real-time monitoring of cloud coverage and …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
2032 Brisbane Olympic Games: how can we achieve climate-positive urban objectives?
Brisbane is the first host city to be contractually bound to deliver a climate-positive Olympic Games in 2032 (Queensland Government, 2023). Most of the 8,000-megawatt coal plants are expected to close by 2032, which requires a viable and sustainable transition to renewable energies (Simshauser, 2024).In this project, we investigate how digital energy services and analytics (DESA) can help a sustainable energy transition for a climate-positive 2032 Brisbane Olympic Games.ReferencesQueensland Government. (2023). All Queensland. All in. 2032 procurement strategy. https://www.forgov.qld.gov.au/__data/assets/pdf_file/0011/404030/Q2032-procurement-strategy.pdfSimshauser, P. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Is battery storage overrated? Achieving grid equilibrium through digital energy services and analytics
The share of renewable energy in electricity generation has globally increased to 28.3%, however, an acceleration of the sustainable energy transition is required to limit worldwide temperature rise (REN21, 2022).Energy storage offers various benefits, such as balancing the mismatch between electricity supply and demand; however, due to its charge/discharge inefficiencies (energy storage results in a loss of at least 10% of electricity in the charge/discharge process), digital solutions are needed to manage grid equilibrium effectively (Watson et al., 2022).In this …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Challenges to data sharing of electric vehicles: alleviating privacy concerns with edge computing
The Australian Government has released Australia’s first National Electric Vehicle Strategy to increase the uptake of electric vehicles (EVs) in Australia (Australian Government, 2023), which has the potential to reduce carbon emissions substantially, given that electricity is produced from renewable energy sources (Degirmenci & Breitner, 2017).Despite environmental benefits like reduced carbon emissions, EV owners become increasingly concerned about their privacy due to enhanced EV connectivity and increased personal data sharing through EV digital services. Edge computing, where data is processed …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
- Research centre(s)
- Centre for Data Science
Driver engagement and risk in automated driving: Advanced data analytics leveraging driver monitoring systems
The project aims to the explore concept of empathic machines in the context of driver monitoring systems (DMS) and automated driving. The successful candidate will contribute to advancing the understanding of driver engagement, situation awareness, and risk through leveraging advancements in data science techniques on vehicle sensor, DMS, and other related datasets.To apply for this position, please submit the following documents:a cover letter outlining your research interests, relevant qualifications, and motivation to join the Empathic Machines projecta detailed curriculum vitae …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Data Science
Centre for Future Mobility
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
Making predictions using simulation-based stochastic mathematical models
Stochastic simulation-based models are very attractive to study population-biology, disease transmission, development and disease. These models naturally incorporate randomness in a way that is consistent with experimental measurements that describe natural phenomena.Standard statistical techniques are not directly compatible with data produced by simulation-based stochastic models since the model likelihood function is unavailable. Progress can be made, however, by introducing an auxiliary likelihood function can be formulated, and this auxiliary likelihood function can be used for identifiability analysis, parameter estimation and …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Predicting good sleep using computer science: Can we use machine learning to find out 'what's the best bed?'
In the Westernised world a person typically spends one third of their life in bed, with more time spent sleeping in a bed than in any other single activity. Sleep amount and quality of sleep have a direct impact on mood, behaviour, motor skills and overall quality of life. Yet, despite how important restful sleep is for the body to maintain good health, there is a comparatively small amount of studies evaluating key multi-factorial determinants of restful sleep in non-pathological, …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
Gamified process-data cleaning
Despite the importance of data quality, it is often compromised. The majority of the time and energy in most data science projects is spent on data cleaning. Process-oriented data mining (process mining) is not an exception. A recent process mining survey shows that more than 60% of the time and effort is spent on data transformation and pre-processing. While, in most cases, the engagement of domain experts is required for accurate data cleaning, it is challenging to engage them in …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Praeclarus process-data quality framework
Praeclarus is an open-source software framework that aims to facilitate data pre-processing for process mining. Process mining is specialised data mining focusing on process-data. It is of high interest to industry, with the market doubling every two years (e.g., increasing from $550M in 2020 to $1B in 2022). This market increase has meant that big companies like Microsoft, SAP, and IBM are acquiring process mining vendors such is Minit, Signavio, and myInvenio.Recent process mining surveys show that more than 60% …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
Overcoming the challenges of sensitive data via synthetic data generation (case study)
In the 21st Century, there is an abundance of data, often containing insights that could benefit a number of stakeholders. However, despite this opportunity, it is often the case that the data is sensitive and can not be released by organisations or government agencies due to privacy concerns. One possible solution to the above dilemma is to instead carefully construct a 'twin' data set that contains similar information (and ideally, the same insights) as the original data set, but without …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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