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 293 matching student topics
Displaying 13–24 of 293 results
Simulation of turbulent fluid flow through a microfluidic device using CFD
Microfluidic devices (MFD) are extensively used in microbial studies. Bacterial cell attachment onto surfaces under flow conditions in laminar regime has been previously studied using a custom designed MFD.As an extension of this study, microbial attachment under turbulent flow is to be studied in a future project. The suitability of current MFD for microbial studies under turbulent flow must be evaluated to adopt / redesign the MFD.A computational fluid dynamics (CFD) analysis is proposed to examine the fluid flow inside …
- Study level
- Honours
- Faculty
- Faculty of Engineering
- School
- School of Mechanical, Medical and Process Engineering
Sport AI
Videos of sport activities are widely available at large scales. AI and its sub-fields, especially computer vision and machine learning, have a great potential to analyse, understand and extract useful information from these videos.This project aims at using AI and its subfields in computer vision and machine learning to develop techniques for analysing sport videos to extract intelligence for players and coaches.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Transport big data analytics: Imputing missing data
The missing data problem is often unavoidable for real-world data collection systems because of a variety of factors, such as sensor malfunctioning, maintenance work, transmission errors, and so on. Filling in missing information in a dataset is an important requirement for many machine-learning algorithms that require a complete dataset as input. Data imputation algorithms aim at filling the missing information in a dataset. Many missing data imputation techniques exist in the literature, with applications demonstrated on various types of datasets. …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Civil and Environmental Engineering
- Research centre(s)
- Centre for Data Science
Efficient parameter estimation for agent-based models of tumour growth
Cancer is an extremely heterogeneous disease, particularly at the cellular level. Cells within a single cancerous tumour undergo vastly different rates of proliferation based on their location and specific genetic mutations. Capturing this stochasticity in cell behaviour and its effect on tumour growth is challenging with a deterministic system, e.g. ordinary differential equations, however, is possible with an agent-based model (ABM). In an ABM, cells are modelled as individual agents that have a probability of proliferation and movement in each …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Making the most of many models
In the age of Big Data, machine learning methods, and modern statistics the adage "all models are wrong but some are useful" has never been so true. This project will investigate data science approaches where more than one model makes sense for the data. Is it better to choose a single model or is there something to be gained from multiple models?This project will look at variable selection methods, penalised regression, Bayesian model averaging and conformal prediction. The research has …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Multi-modal sentiment analysis
In deep learning models, language models and word embedding methods have become popular to understand the context of text data. Popular language models such as BERT have limitations in terms of the token length. There exist some corpora that have longer text with an average of 1000 tokens. Additionally, these corpora are text-heavy and only include some images.In our prior works, we have developed several multi-modality models on social media datasets.
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Overcoming the challenges of sensitive data via synthetic data generation (case study)
In the 21st Century, there is an abundance of data, often containing insights that could benefit a number of stakeholders. However, despite this opportunity, it is often the case that the data is sensitive and can not be released by organisations or government agencies due to privacy concerns. One possible solution to the above dilemma is to instead carefully construct a 'twin' data set that contains similar information (and ideally, the same insights) as the original data set, but without …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Using time-controlled grazing to sequester carbon in Queensland rangelands
Time-controlled grazing (TCG), or cell grazing is a management strategy in which cattle are stocked and rotated across small paddocks or “cells” according to fodder availability. Grazing takes place in short durations at high stocking densities, in an effort to mimic the grazing patterns of wild ungulate herds.This management strategy has gained traction in recent years due to claims that it improves both pasture productivity and diversity, whilst also increasing long-term carbon pools. Limited data is available on the impact …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Biology and Environmental Science
- Research centre(s)
- Centre for Agriculture and the Bioeconomy
Capturing the impact of patient variability in a novel cancer treatment
In 2015, the Food and Drug Association (FDA) approved a lab-engineered virus for the treatment of melanoma (skin cancer). Since then, there has been a significant increase in the number of lab-grown viruses that are being tested in clinical trials as potential treatments of cancer. Unfortunately, it seems that a large number of patients in these clinical trials fail under this treatment and currently there is no way to distinguish between responders and non-responders to treatment.Fortunately, we can use mathematics …
- Study level
- Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Analysis of professional squash matches
This project concerns computer vision and statistical analysis of performance in professional level matches in the game of squash.The goal is to use computer vision and existing systems to capture and analyse patterns of play, allowing coaches and professional players to develop strategies to improve performance, to counter particular types of play and even to tailor game plans to attack individual opponents.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Visualisation and sonification for genomic data sets
Successive revolutions in sequencing technology over the past two decades have led to an explosion in the availability of genomic data. Analysing biological datasets and identifying relationships within them is challenging - some of the process can be automated but interactive exploration offers a number of advantages, and supports serendipitous discovery.This project looks at visual analytics and sonification - the use of sound and musical encodings - to enhance our understanding of biological networks.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Facilitating gaining trust in AI
Artificial intelligence (AI) technologies are automating service delivery in many sectors. Businesses have shown interest in using these technologies for delivering complex services in a way that meet the unique needs of customers. The technology gained more popularity particularly during Covid-19 outbreak, as it helped organisations to become more efficient in service delivery and increased service availability for customers / service applicants. However, gaining managers’ and users’ trust in these systems has always been a significant challenge. Particularly, managers and …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Information Systems
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