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 87 matching student topics
Displaying 25–36 of 87 results
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
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
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
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
Quantum machine learning
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
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Novel algorithms for microbiome data
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
- Faculty
- Faculty of Health
- School
- School of Biomedical Sciences
- Research centre(s)
-
Centre for Microbiome Research
Advanced numerical modelling to study fluid flow and heat transfer of ground-mounted photovoltaic panels for the power generation industry
The increase in global energy demand necessitates further advancement in photovoltaic (PV) systems. Advancements in PVs could potentially play a role to help meet the Paris Agreement of limiting global temperature increase to below 2°C.The performance of ground-mounted PV panels commonly found in solar farms depends on a myriad of factors such as tilt angle, microclimate i.e. wind loads, shading, solar irradiance, and dust deposition. This project aims to develop an advanced numerical model, namely computational fluid dynamics (CFD), backed …
- Study level
- PhD, Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
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
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.