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Found 21 matching student topics

Displaying 13–21 of 21 results

Scene Understanding for Underwater Imagery

Underwater ecosystems, including coral reefs and seagrass meadows, play a critical role in maintaining marine biodiversity, providing coastal protection, and supporting fisheries and tourism economies that millions depend upon globally. These habitats are increasingly vulnerable to climate change, pollution, and other anthropogenic impacts, demanding urgent efforts to monitor and restore them. Accurate scene understanding of underwater imagery enables fine-scale ecosystem monitoring across spatial and temporal scales, supporting essential activities such as habitat and biodiversity assessment, validation of aerial and remotely …

Study level
PhD
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Development of a machine learning algorithm for high throughput cell response data in drug therapy

High-throughput screening assays are essential for accelerating drug discovery, but current assays often rely on endpoint measurements that do not capture the dynamic response of cells to drug treatment. Machine learning algorithms (MLAs) have the potential to enable real-time, high-throughput monitoring of cell response to drug treatment by analyzing complex datasets generated by multiplexed live-cell assays. This research project aims to develop an MLA for enabling high throughput cell response data in drug treatment. The project will involve three main …

Study level
Honours
Faculty
Faculty of Engineering
School
School of Computer Science
Research centre(s)
Centre for Biomedical Technologies
Centre for Biomedical Technologies

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

Investigating the application of sustainable AI practices in construction

The construction industry plays a vital role in the global economy and there is a growing interest in utilising artificial intelligence (AI) to improve its productivity and efficiency. Despite the industry's significant contribution to the economy, it has faced challenges such as large cost overruns, extended schedules, and quality concerns. Nevertheless, AI is making significant strides to remove these issues by revolutionising various aspects of the construction industry. This is evident from enhancing project planning and design to improving construction …

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

Light, circadian rhythms and Parkinson’s disease

Up to 98% of patients with Parkinson’s Disease (PD) have non-motor symptoms (Poewe et al. Nature Rev Dis 2017, 3: 17013) and of those, circadian and sleep disorders are the most common (for review, Gros & Videnovic. 2020, Clin Geriatr Med 36: 119). These symptoms become increasingly prevalent during the course of PD and are key determinants affecting quality of life, advancement of overall disability and placement in nursing homes (Shapira et al. Nat Rev Neurosci 2017,18:435). Circadian and sleep …

Study level
PhD
Faculty
Faculty of Health
School
School of Biomedical Sciences

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

Artificial intelligence (AI) to balance fluctuations of intermittent renewable energy sources

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
Faculty
Faculty of Science
School
School of Information Systems
Research centre(s)
Centre for Data Science

Digital Leadership Competencies for AI Adoption

Identify the specific competencies and skills that leaders need to effectively lead AI adoption initiatives in organisations. Research can focus on areas such as data literacy, AI literacy, critical thinking, decision-making, and the ability to manage and interpret AI-driven insights. Develop frameworks for assessing and developing these competencies in leaders.

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

Centre for Behavioural Economics, Society and Technology

Drone and satellite Artificial Intelligence

Satellite and drone/UAV data has a great potential to provide large-scale analytics for many domain applications. However, the wide range of data of diverse nature (e.g., optical vs. SAR, high-resolution vs. wide-coverage, mono- vs. hyper-spectral, 2-D vs. 3-D) also poses significant challenges for analytics.Deep learning holds great promise to deal with these tasks. While the number of research in this area is increasing, there still exists challenges such as co-learning of multimodal data, limited data annotation, and uncertainty in the …

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

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