Student topics

Consumers generally express positive attitudes towards renewable energies, recognizing system values such as environmental benefits and sustainability advantages (Zhang et al., 2024). However, the actual adoption and use of renewable energy services may not align with their expressed preferences.This paradox stems from various customer value-related barriers, such as high upfront costs, limited awareness, and concerns about reliability or convenience, which hinder widespread consumer adoption of renewable energy solutions (Jridi et al., 2016).In this project, we analyse the consumer adoption paradox …

Study level

PhD, Master of Philosophy, Honours

School

School of Information Systems

Research centre(s)

Centre for Behavioural Economics, Society and Technology

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

School

School of Information Systems

Research centre(s)

Centre for Data Science

Artificial intelligence (AI) can play a significant role in analyzing and predicting energy consumption and production patterns from renewable sources such as solar and wind (Lyu & Liu 2021). This is particularly important due to the key challenge of intermittency, where major renewable sources for electricity, such as solar and wind, are subject to the inconsistencies of the weather (Watson et al., 2022).In this project, we investigate how AI and machine learning algorithms can optimize smart grids and other components …

Study level

PhD, Master of Philosophy, Honours

School

School of Information Systems

Research centre(s)

Centre for Data Science

The increasing frequency and intensity of extreme weather events, such as intense heatwaves, floods, and bushfires, is triggering disruptive disasters that have a significant impact on communities, ecosystems, and economies. While our national climate change adaptation strategy places a paramount focus on enhancing community resilience, it is crucial to recognise that not all communities face climate risks in the same manner. Diverse communities exhibit varying capacities to respond and adapt to distinct climate hazards. This reality underscores the imperative for …

Study level

Honours

School

School of Architecture and Built Environment

Research centre(s)

Centre for the Environment

The Australian Commission on Safety and Quality in Health Care has highlighted that reducing avoidable hospital readmissions supports better health outcomes, improves patient safety and leads to greater efficiency in the health system. Previous studies have reported that up to 11% of the emergency (ED) population are 'heavy users' with a higher prevalence of psychosocial problems and often co-existing chronic medical conditions. All Australian governments have committed to reforms under the National Health Reform Agreement Addendum,1 and the ability to …

Study level

PhD, Master of Philosophy, Honours

School

School of Computer Science

Research centre(s)

Centre for Data Science

In process mining, sequence analysis algorithms are used to discover computational models of process data, and to analyse them. The insights from these models and analysis then improve the processes in organisations in many real-life domains - from manufacturing, to government, to healthcare. Haskell is a powerful functional programming language well suited to problems involving formal reasoning and pattern matching. This project would advance process mining research by building high-quality, high performance libraries in Haskell for fundamental process mining activities …

Study level

Honours

School

School of Information Systems

Software vulnerabilities have been considered as significant reliability threats to the general public, especially critical infrastructures. Many approaches have been proposed to detect vulnerabilities in source code to avoid any damages they pose when exploited. Conventional approaches include static analysis and dynamic analysis. Static analysis uses pre-defined patterns or vulnerability dataset to scan and examine software source code to identify potential vulnerable code snippets. These patterns are manually crafted or identified by software developers or security experts, which are time-consuming. …

Study level

PhD, Master of Philosophy, Honours

School

School of Computer Science

Research centre(s)

Centre for Data Science

Quantum machine learning is the integration of quantum algorithms within machine learning programs with great potential to solve complex problems. For instance, Google’s Sycamore processor (61) performs in 200 seconds a task that would require 10,000 years using a classical computer.

Study level

PhD

School

School of Electrical Engineering and Robotics

As part of an ARC Future Fellowship project awarded to Luis Pedro Coelho, we aim to study small proteins with the aim of better understanding them and laying the groundwork for exploiting them for biotechnological purposes. Small proteins (up to 100 amino acids, but often much shorter) have vital roles in all areas of life, but have been neglected in research due to lack of methods.Particular projects in this topic include developing methods for determining function based on genomic context, …

Study level

PhD, Master of Philosophy, Honours

School

School of Biomedical Sciences

Research centre(s)

Centre for Microbiome Research

Metagenomics data is complex, high-volume data and keeps evolving, requiring novel computational method development as the wetlab approaches changes and databases grow. Thus, novel computational methods are required to take advantage of them.There are several potential projects under this topic, including:using deep learning to improve metagenomics assemblydeveloping better tools to analyse the presence of resistance genes in metagenomics datadeveloping approaches for estimating the quality of genomes from novel generation sequencespredicting the function of small sequences using more than just sequence.Interested …

Study level

PhD, Master of Philosophy, Honours

School

School of Biomedical Sciences

Research centre(s)

Centre for Microbiome Research

Join a world-leading research team examining how recommender systems are shaping personalised and shared experiences of algorithmic culture in Australia. The project is focused on TikTok and engages with both professional TikTok creators and users using innovative computational and traditional research approaches.The empirical work is structured into three streams:In the Platform Stream we observe the type of content TikTok recommends on the least-personalised version of the platform, to create a close-to-generic baseline of the Australian experience of algorithmic culture on …

Study level

PhD

School

School of Communication

Research centre(s)

Digital Media Research Centre

In process mining, we perform computational analyses of sequential data in order to help organisations improve, in settings from ride-sharing platforms to government departments. In social sequence analysis, we perform computational analysis on sequential data to understand small or large structures in society, such as the progress of careers of 18th century German musicians, or the progress of nations through different stages of economic development.In both process mining and social sequence analysis, calculation of "alignments" for is a key technique …

Study level

Honours

School

School of Information Systems