The Vacation Research Experience Scheme (VRES) provides eligible students with the opportunity to participate in a research project. If you're interested in research and thinking of pursuing a research degree the scheme is an opportunity to see if research is right for you. Further information about the scheme is available on HiQ.
QUT offers a diverse range of student topics for VRES. Search to find a topic that interests you.
Found 208 matching student topics
Displaying 73–84 of 208 results
Mathematical and computational methods for diffusion magnetic resonance imaging
An investigation into aspects of Diffusion Magnetic Resonance Imaging (diffusion MRI) using mathematical techniques, numerical simulation and machine learning. Depending on the background and interests of the student this can involve deriving solutions to PDEs, coding numerical simulations based on random walks, generating synthetic data and using machine learning to find links with model parameters and signals.
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre
- Centre for Data Science
Exploring machine learning algorithms for vegetation species classification
Testing different Machine learning algorithms and/or deep learning architectures to classify vegetation species from aerial imagery
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
- Research centre
- Centre for Robotics
Numerical solution of partial differential equations
Spectral methods for solving partial differential equations (PDEs) on irregular domains.
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
Characterisation of Ships from their Wakes
A mathematical and computational investigation of an inverse problem that takes as input a signal measured from a ship's wake, and infers characteristics about the vessel that produced it.
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre
- Centre for Data Science
Cybersecurity Threat Detection with ML and Process Mining
The research idea involves combining machine learning (ML) and new process mining (PM) techniques to facilitate more accurate detection of interesting anomalies in large event logs extracted from cybersecurity data. Although ML applications in PM are quite common, methods that use process model discovery and conformance checking to improve the performance of ML models are not well-explored. While ML can uncover complex and meaningful patterns, in-depth analysis of processes can reveal valuable insights, providing additional information that can augment the …
- Faculty
- Faculty of Science
- School
- School of Information Systems
Ore-Grade Estimation from Hyperspectral Data using Machine Learning
This project involves using machine learning algorithms to predict the mineral content and quality (ore grade) of geological samples from hyperspectral imaging data.Hyperspectral sensors capture detailed spectral information across hundreds of narrow wavelengths, revealing the unique "spectral fingerprints" of different minerals that aren't visible to the naked eye. Each mineral reflects and absorbs light differently across the electromagnetic spectrum, creating distinctive patterns.The machine learning component analyzes these complex spectral signatures to automatically estimate ore grades - essentially predicting how much …
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Reinforcement Learning for Fair and Ethical AI Systems
This project studies how reinforcement learning (RL) can help make automated decisions fairer, especially using methods like contextual bandits and deep RL. Instead of fixing fairness after training, fairness is built into the learning process to create more equal outcomes for different groups. The focus is on important areas like hiring, healthcare, and finance, where biased AI can cause real harm. The aim is to reduce unfair bias while keeping the system accurate, helping create AI that is both effective …
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Efficient Predictive Modelling using Physics Informed Networks.
This research explores how advanced physics-informed neural network models can guide the development of simplified yet accurate predictive systems across scientific and engineering domains. The work spans machine learning, computational physics, and applied mathematics, addressing the critical challenge of creating efficient models that maintain physical consistency and predictive reliability.Recent advances in neural operator learning and physics-informed architectures have demonstrated potential for dramatically reducing model complexity while preserving domain-specific knowledge. This research investigates generalizable frameworks for developing simplified predictive models that …
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Designing an Interactive Dashboard for Mapping and Modelling Self-Storage Demand in Australia and New Zealand
This interdisciplinary project is part of the Building 4.0 CRC – Project #97: Smart Storage Analytics, funded by the Cooperative Research Centres (CRC) Program. It focuses on the design and enhancement of a web-based geospatial dashboard to support the analysis of self-storage facilities across Australia and New Zealand.The dashboard enables users to explore spatial patterns, compare facility characteristics, and understand demand dynamics at both regional and national scales. It brings together concepts from spatial analysis, data visualisation, and user-centred design …
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
Digital Twin in Medical or Sports Context
Digital Twins are virtual representations of physical systems that integrate real-time data to mirror, simulate, and analyse behaviour. In medical and sports domains, they offer diverse possibilities for innovation. This project focuses on exploring their potential applications, aiming to understand how such models can be utilized in these evolving fields.
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
RNAi mediated crop protection against lepidopteran pests
To test the efficacy of transient Trans-Kingdom RNAi technology for improved plant resistance against fall armyworm, an important agricultural pest.
- Faculty
- Faculty of Science
- School
- School of Biology and Environmental Science
- Research centre
- Centre for Agriculture and the Bioeconomy
Exploring how engineering students choose their major and develop their professional identity
Choosing an engineering major is a pivotal decision in the academic and professional trajectory of future engineers. This choice not only shapes the technical competencies students will acquire but also plays a foundational role in the development of their professional identity - how they come to see themselves as engineers. Understanding why and how students select their major can offer critical insights into the formation of their professional identity, as well as inform recruitment and retention efforts within engineering education.Professional …
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
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If you have questions about the Vacation Research Experience Scheme (VRES), the application process, finding a topic or anything else, get in touch with us today.