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 631 matching student topics
Displaying 241–252 of 631 results
Developing bio-additives for PFAS waste management
Have you ever wondered how we deal with harmful pollutants like PFAS, the so-called 'forever chemicals'? PFAS are found in everyday items like non-stick cookware, waterproof clothing, and firefighting foam, but they don’t break down in the environment. Over time, they build up in water, soil, and even our food, creating serious risks for human health and ecosystems.Managing PFAS is one of the biggest environmental challenges of our time. Traditional methods to remove them are expensive and can produce more …
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
Optimising landfill leachate treatment with innovative bioreactors
Landfill leachate, a highly polluted liquid, can pose serious risks to the environment and public health if not treated effectively. Without proper management, it can contaminate groundwater and surface water, threatening ecosystems and water resources.This project focuses on using innovative bioreactors to treat landfill leachate efficiently and sustainably. By optimizing these reactors, we aim to remove harmful pollutants like ammonia, addressing a key challenge in managing high-strength wastewater. Your work will contribute to practical solutions for real-world environmental problems.
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
Advanced biosolid treatment: volume reduction and PFAS mitigation
PFAS, often called 'forever chemicals', are among the most persistent and harmful pollutants found in the environment. These substances accumulate in biosolids, a nutrient-rich by-product of wastewater treatment, making it challenging to safely reuse or dispose of them. Biosolid management becomes even more complex due to the large volume produced by treatment facilities, creating a pressing need for innovative solutions that address both PFAS contamination and waste volume reduction.This project focuses on cutting-edge approaches to treat biosolids, reduce their volume, …
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
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
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
Hierarchical forecasting: forecasting a collection of time series
Hierarchical forecasting is a method used to generate forecasts at multiple levels of aggregation within a structured hierarchy. This technique is particularly valuable in situations where data can be organised into a hierarchy based on different dimensions, such as geography, product categories, or time. The approach ensures that forecasts at the top levels (e.g. total sales) align with forecasts at the lower levels (e.g. regional or product-level sales), creating a coherent and consistent forecasting process across the entire hierarchy.In many …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Bayesian focused learning
Forecasting renewable energy production is crucial for ensuring stable and sustainable energy grids. Traditional approaches often involve a two-stage process: first, energy production forecasts are generated, then decisions, such as how much energy to produce from various sources (wind, solar, fossil fuels), are made based on those forecasts. This disjointed process, where forecast accuracy and decision-making optimization are treated separately, can lead to sub-optimal outcomes due to conflicting objective functions.The goal of this project is to bridge these stages by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Optimising inventory control and demand forecast accuracy though multi-objective optimisation
In today’s competitive business environment, effective inventory management and accurate demand forecasting are critical for minimising costs and maximising profitability. This project aims to address these two challenges simultaneously by applying a multi-objective optimisation approach. The primary objectives are to improve demand forecast accuracy while optimising inventory control decisions, balancing trade-offs between conflicting business goals such as minimising stockouts, reducing excess inventory, and maintaining customer service levels.Traditional approaches to inventory management and demand forecasting often treat these processes separately, which …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Bridging the gap: leveraging AI to improve healthcare access
Access to quality healthcare remains a significant challenge in many parts of the world, often due to geographic and financial barriers. This research explores how artificial intelligence (AI) can address the challenges of geographic and financial barriers in accessing healthcare. The project will focus on developing AI-powered solutions that enhance healthcare delivery, increase patient engagement, and reduce costs
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Public Health and Social Work
Unveiling the explainability imperative in medical AI
As AI systems become increasingly prevalent in medical applications, the need for explainable AI (XAI) has become crucial. This research investigates the critical issue of explainability in medical artificial intelligence (AI) systems. This project investigates methods for improving the interpretability and transparency of AI models used in medical diagnosis, treatment planning, and prognosis prediction. Understanding the reasoning behind AI-driven decisions is essential for building trust among healthcare professionals and ensuring patient safety.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Public Health and Social Work
Enhancing clinical decision-making through AI-assisted agents
Artificial Intelligence (AI) has shown tremendous potential in revolutionizing healthcare delivery. This research focuses on developing AI agents that can augment clinical decision-making processes, ultimately improving patient outcomes. The project aims to explore and design novel AI architectures that integrate disparate medical data sources, providing context-aware recommendations for diagnosis, treatment planning, and care coordination. Despite the promising applications of AI in healthcare, significant challenges remain in integrating these technologies into clinical practice effectively and safely.
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Health
- School
- School of Public Health and Social Work
Spatio-Temporal Forecasting of renewable energies
This project aims to develop short-term (up to 24 hours ahead) forecasting models that take into account the spatial as well as temporal information in wind farms and solar farms. Such models are useful for operational planning in farms and stabilising the network.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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