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.

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Found 30 matching student topics

Displaying 13–24 of 30 results

Process-data governance patterns

Data is recognised a strategic asset for organisations. There is a growing need to manage the voluminous data an organisation is exposed to in order to use it for decision-making.Of particular significance is process data, which consists of information about the execution of processes. Such information is used to uncover behaviour of processes within an organisation. This brings forth the significance of data governance. Data governance is the exercise of control and authority over management of data. Despite its significance, …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Information Systems

Understanding psychosocial factors for addressing mental health concerns in the construction industry

Construction workers are at an increased risk of suicide and experience higher rates of poor psychological health. This research investigates the many psychosocial factors that can contribute to worker psychological (and general) wellbeing. This includes social support, social capital, personal networks, work-related conditions, and work-life-balance. Managing the psychosocial wellbeing of workers is as critical to addressing physical risks associated with construction work.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Business and Law
School
School of Management

Understanding public perceptions of the sustainable energy transition: a social network analysis

The challenge to keep global warming to 1.5°C above pre-industrial levels has become even greater due to a continued increase in greenhouse gas emissions (IPCC, 2023). One major challenge is the shift from fossil fuels to renewable energy to reduce emissions (Gholami et al., 2016). The share of renewable energy in electricity generation has increased to 28.3%, however, an acceleration of the pace of the transition is required to limit global temperature rise (REN21, 2022).In this project we investigate public …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Information Systems
Research centre(s)
Centre for Data Science

Representation learning for anti-microbial resistance

This project is about using neural network models help us understand Anti-Microbial Resistance (AMR), a phenomenon in which bacteria adapt to reduce the effectiveness of antibiotics, usually through a process known as Lateral or Horizontal Gene Transfer - where genes are included in the organism from other sources.Our focus will be on learning compact vector representations of biological sequences known to be associated with AMR genes. By encoding DNA sequences in this way we can more rapidly identify AMR genes …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Board interlocks and firm decisions

Board interlocks research is one of the most vibrant areas in corporate governance research. A board interlock is a tie created by two firms sharing a common director. In other words, a director can hold multiple directorships in more than one firm. Board interlocks reflect complex inter-organisational relationships which play an important role in determining a firm’s strategies and structures.Prior research finds that board interlocks have an impact on reducing environmental uncertainty, gaining access to diverse and unique information, diffusing …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Business and Law
School
School of Accountancy

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

Road map to local circular communities: strategies, barriers, enablers.

The textile industry is one of the world’s largest, with global sales in 2016 of USD 1.5 trillion. It is also one of the most polluting industries, producing 20% of global wastewater, and contributing to 10% of carbon emissions. Fashion generates large amounts of waste, and has negative social and health impacts for workers.Circular economy would address these issues, keep clothes and textiles at their highest value and keep clothes in continuous circulation. The transformation of the sector requires a …

Study level
PhD
Faculty
Faculty of Creative Industries, Education and Social Justice
School
School of Design
Research centre(s)

Design Lab

Physics-informed reinforcement learning for complex environments, using graph neural networks

Neglecting to incorporate physics information into world models for reinforcement learning leads to reduced adaptability to dynamic and complex environments and overall learning outcomes.In this project, we endeavour to develop and implement learnable models in reinforcement learning (RL) based on graph neural networks (GNNs). These models will integrate object and relation-centric representations to enable accurate predictions, strong generalization, and system identification in complex, dynamical systems. Additionally, we will focus on leveraging extensive world knowledge or physics information to refine representations …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

Hostile Interaction Design

Hostile interaction design has emerged as a critical issue in contemporary digital environments, where design decisions increasingly prioritise institutional and corporate interests over the needs of users and citizens. Drawing parallels to hostile architecture—where physical spaces are designed to control behaviour, such as anti-homeless spikes or uncomfortable public seating—hostile interaction design manifests in digital systems as frustrating, impersonal, or obstructive experiences. These designs often shield corporations and governments from accountability, erecting barriers that prevent users from seeking help, lodging complaints, …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Creative Industries, Education and Social Justice
School
School of Design
Research centre(s)
Digital Media Research Centre
Design Lab

Understanding local government artificial intelligence policy landscape

Artificial intelligence (AI) is driving transformation across all areas of society today. An umbrella term encompassing a range of technologies both sophisticated and simple that are used to make predictions, inferences, recommendations, or decisions with data. AI is used in many products and services that people use, interact with, or are impacted by every day. It already in place of local government and assisting government officials in providing services effectively and conduct their activities more efficiently to the public. The …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Engineering
School
School of Architecture and Built Environment

Injury is a major contributor to mortality, morbidity, and permanent disability, and imposes a significant burden on the Australian health system. A better understanding of the burden, models of care, and economic drivers of injury will help design cost-effective injury prevention and treatment strategies to minimize the incidence and burden of the disease while improving injury outcomes.

Study level
PhD
Faculty
Faculty of Health
School
School of Public Health and Social Work
Research centre(s)
Centre for Healthcare Transformation
Australian Centre for Health Services Innovation

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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