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 76 matching student topics
Displaying 13–24 of 76 results
Deep learning for next-generation visual place recognition: sequence matching in robotic navigation
Are you interested in contributing to the advancement of robotics and navigation systems? In this VRES project, you will explore the cutting-edge field of visual place recognition (VPR).In both human and robotic navigation, understanding our location is essential for various tasks, such as obtaining resources, commuting, and socialising. The future of robots and augmented reality devices depends on reliable localisation systems. VPR is a critical aspect of robotics that allows for navigation and decision-making without relying on satellite systems, which …
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre(s)
- Centre for Robotics
Robot learning for navigation, interaction, and complex tasks
How can robots best learn to navigate in challenging environments and execute complex tasks, such as tidying up an apartment or assist humans in their everyday domestic chores?Often, hand-written architectures are based on complicated state machines that become intractable to design and maintain with growing task complexity. I am interested in developing learning-based approaches that are effective and efficient and scale better to complicated tasks.Especially learning based on semantic information (such as extracted by the research in semantic SLAM above), …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Automatic Generation of Software Vulnerability Datasets for Machine Learning
In recent years, machine learning has enjoyed profound success in a range of interesting applications such as natural language processing, computer vision and speech recognition. It has been possible mainly due to, in addition to better computing resources, the availability of large amounts of training datasets to these applications. However, in software security research, the lack of large datasets is an open problem that makes it challenging for machine learning to reason about security vulnerabilities found in real-world software. The …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Science
- School
- School of Computer Science
Information retrieval and coding methods for large scale bioinformatics
Advances in sequencing technologies over the past two decades have led to an explosion in the availability of genomic sequence data and an increasingly urgent need for scalable clustering and search facilities. One approach is to encode sequences as binary vectors in a high-dimensional space, simplifying the comparison and allowing it to be computed very rapidly using bit-level operations.Coupled with these ideas is the need to provide clustering methods and efficient indexing and lookup in response to search queries. One …
- Study level
- PhD, Master of Philosophy, Honours, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
Representation learning for anti-microbial resistance
This project is about using neural network models help us understand Anti-Microbial Resistance (AMR), a phenomenon in which bacteria adapt to reduce the effectiveness of antibiotics, usually through a process known as Lateral or Horizontal Gene Transfer - where genes are included in the organism from other sources.Our focus will be on learning compact vector representations of biological sequences known to be associated with AMR genes. By encoding DNA sequences in this way we can more rapidly identify AMR genes …
- Study level
- PhD, Master of Philosophy, Honours, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Computer Science
- 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, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
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, Vacation research experience scheme
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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, Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Predicting how fast the next Olympic medalists will swim
A 3-year strategic partnership on sports data science between the Centre for Data Science (CDS), the Australian Institute of Sport (AIS) and the Queensland Academy of Sport (QAS) is currently under way. With a drive towards data informed decision making across the high performance sports network nationally, a number of collaborative, interdisciplinary research and scholarship opportunities ranging from VRES, to honours, masters and PhD have developed.
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Sampling to optimise training data
Training data is needed for fitting supervised machine learning models but may not always be plentiful, especially when labelling needs to be performed manually. In such a situation we may wish to target the new sample cases to be labelled to improve model performance for the least additional resource.In this project we’ll look at how active learning techniques - techniques which make use of the model outcomes – can be used to effectively determine a sampling strategy.
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Assessing the quality of training
Understanding the impact that training data quality has on model generation is important for industry’s looking to engage in responsible data science.In this project we’ll look at the use of instance space analysis for assessing the quality of training data and algorithm performance.
- Study level
- Vacation research experience scheme
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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, Vacation research experience scheme
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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