Student topics

A fully funded PhD scholarship is available in the School of Civil and Environmental Engineering at Queensland University of Technology (QUT) as part of a newly awarded Australian Research Council (ARC) Discovery Project titled 'Shaping net-zero cities with safe and efficient micromobility solutions'.This PhD project will investigate the behavioural and safety interactions between pedestrians, micromobility users (e.g. e-scooters and e-bikes), and other road users in shared urban environments. The research will combine AI-based video analytics, trajectory analysis, behavioural modelling, and …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Civil and Environmental Engineering

Modelling non-stationary dynamics from high-frequency time-series data remains challenging. These signals often exhibit complex temporal and spectral structure, while observations are typically noisy, incomplete, and affected by changing operating conditions, making reliable prediction and representation learning difficult.This PhD project, offered at Queensland University of Technology (QUT) in collaboration with industry partners, focuses on learning representations and dynamics from multimodal time-series data.The research will explore deep approaches including sequence models, transformer-based architectures, anomaly detection, graph neural networks, and self-supervised learning, with …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

The assessment of medical graduate competency is a cornerstone of medical education and a critical safeguard for patient safety. Newly qualified physicians must demonstrate a broad range of skills and knowledge, including diagnostic reasoning, clinical decision-making, communication, procedural skills, and professionalism before independently practicing medicine. Traditional assessment methods often include standardized multiple-choice examinations, objective structured clinical examinations (OSCEs), direct observation of procedural skills (DOPS), and portfolio reviews. While these methods offer valuable insights, they have inherent limitations. Standardized tests may …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Health

School

School of Public Health and Social Work

Research centre(s)

Centre for Data Science

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

The demand for 3D scene understanding through point clouds is rapidly growing in diverse applications, including augmented and virtual reality, autonomous driving, robotics, and environment monitoring. However, the field faces challenges due to limited data availability and predefined categories. Training deep 3D networks effectively for sparse LiDAR point clouds requires significant amounts of annotated data, which is both time-consuming and expensive. Building on the advancements in 2D models that leverage the power of image and language knowledge, our project aims …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics