Student topics

Organisations are increasingly moving toward self-service technology (where consumers deliver services themselves using technological interfaces). While this increases organisational efficiencies, it has the potential to significantly impact customer engagement with the organisation. We're seeking a Masters or PhD student to investigate the human factors involved in self-service technology delivery: How do situational factors impact consumers' adoption of self-service technologies? What motivators and inhibitors impact self-service technology usage? Do consumers always respond appropriately within self-service environments? The successful applicant for this …

Study level

PhD, Master of Philosophy

A popular topic in differential geometry involves studying the singularity structure of geometric flows. The most well-known example is mean curvature flow. In this example, surfaces evolve according to a flow rule that relates the speed of the surface to its curvature. Certain surfaces will evolve until singularities occur in finite time, and these singularities can be studied using similarity solutions and asymptotic analysis.In this project, a different perspective is applied to these problems, namely the use of complex variable …

Study level

Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Mathematical Sciences

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

Meta-heuristics are powerful search algorithms for solving intractable optimization problems. There are many population based approaches, like genetic algorithms, evolutionary algorithms, particle swarm, etc. but most of these have a static population size.Viruses arise and attack populations periodically. They typically appear when populations become abundant. Viruses infect population members, and often reduce the number of individuals. Viruses create spaces for more individuals and balance competition.The concept of viruses may be mimicked and could be a useful optimization paradigm.

Study level

Honours

Faculty

Faculty of Engineering

School

School of Mechanical, Medical and Process Engineering

Developing and supporting flexible or customised work arrangements is a key HR strategy in many organisations and has been shown to have benefits for both business and individual employees. However, research has also demonstrated the significant problem of policy-utilisation gaps in relation to flexible work, including concerns about unequal access, low uptake amongst men and penalties that result from adjusting standard working arrangements.Student projects are available in this field of research and there is the possibility of utilising existing and …

Study level

PhD, Master of Philosophy, Honours

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

The project aims to the explore concept of empathic machines in the context of driver monitoring systems (DMS) and automated driving. The successful candidate will contribute to advancing the understanding of driver engagement, situation awareness, and risk through leveraging advancements in data science techniques on vehicle sensor, DMS, and other related datasets.To apply for this position, please submit the following documents:a cover letter outlining your research interests, relevant qualifications, and motivation to join the Empathic Machines projecta detailed curriculum vitae …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Civil and Environmental Engineering

Research centre(s)

Centre for Data Science
Centre for Future Mobility

Recent advances in computer vision have demonstrated superhuman performance on a variety of visual tasks including image classification, object detection, human pose estimation and human analysis. However, current approaches for achieving these results center around models that purely learn from large-scale datasets with highly complex neural network architectures. Despite the impressive performance, pure data-driven models usually lack robustness, interpretability, and adherence to physical constraints or commonsense reasoning.As in the real world, the visual world of computer vision is governed by …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

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

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

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

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Organised sport is primarily community based in Australia; and the benefits of sport participation to individuals and communities are well documented. However, there is also evidence that participating in organised high-performance sporting programs is associated with psychological distress, elevated relative to community norms, which would usually warrant a need for care by a health professional. As such a case for improvement in mental health education and practice in sporting communities exists.Mental Health First Aid (MHFA) is a standardised, psychoeducational programme …

Study level

PhD, Master of Philosophy

Faculty

Faculty of Health

School

School of Exercise and Nutrition Sciences