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 57 matching student topics
Displaying 37–48 of 57 results
Assessing the quality of cluster analysis
Machine learning cluster methods are common classification methods, but methods for assessing performance are limited as are methods for explaining how they work. Exploring methods for both assessing and explaining performance are the subject of this research with application to real-world contexts with the Australian Bureau of Statistics.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Machine Learning-based pedictive tool for energy storage
The fundamental idea behind the ML approach is to analyze and map the relationships between the physical,chemical, and energy storage properties of materials with their associated output data. This early understanding of the energy storage capabilities through the ML approach helps the material scientists to clearly understand, discover, and optimize the fabrication process to develop highly efficient energy storage systems. It also provides key steps in the device fabrication process omitting excessive experimental stages.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Chemistry and Physics
- Research centre(s)
- Centre for Materials Science
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
Decision optimisation in energy supply chain
this project aims to develop integrated forecasting and decision optimisation models for renewable energies.
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Probabilistic forecasting of energy
This project aims to develop probabilistic forecasting models for renewable energies vi a Bayesian approach. The models will be developed for very short term and short-term (10 minutes to 24 hours ahead).
- Study level
- PhD
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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
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
Exploring the potential of M-assisted survey estimators
The Australian Bureau of Statistics (ABS) conducts surveys to collect information from individuals, households and businesses in order to produce statistics and data products to help inform decision-making. Unlike a census, in which an entire population of interest is enumerated (e.g., all individuals residing in Australia), a survey collects information from only a sample (subset) of a population of interest. Estimators are then used to estimate quantities related to the population of interest using information from the sample. Currently, the …
- Study level
- PhD
- 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
Achieving a sub-micrometer surgical robot end-effector via hybrid sensing
When operating with a tool within the human body in the context of a medical procedure, it is crucial to be able to keep track of the pose of the tool. This project will develop a hybrid approach to end effector pose estimation by combing optical tracking with other sensor inputs (e.g. force, sound, acoustic emissions) to compliment and improve tracking accuracy with applications towards orthopaedic surgical robots. This project is part of a broader collaboration with industry partner Stryker.
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
- Research centre(s)
- Centre for Biomedical Technologies
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
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
Contact us
If you have questions about the best options for you, the application process, your research topic, finding a supervisor or anything else, get in touch with us today.