PhD Projects SEIT

Scholarships of $35,000 (AUD) are available for PhD students  who have achieved Honours 1/High Distinction in their UG program and/or have completed a Masters by Research.

System Architecture Evaluation and Optimization at Early Conceptual Design Stage

Defence systems acquisition is fraught with all sorts of financial, technical and political risks. The most effective way of mitigating risks associated with acquisition of complex systems is through identification of these risks as early as possible. This project’s aim is to facilitate optimal decision making for multi stakeholder system design.

Big Data and CPS in Project Scheduling

In the presence of increasingly dynamic environments, frequent uncertainties, high customer specifications, strict project deadlines, and stricter requirements on sustainability and scarcity of resources, modern project managers are challenged in their ability to schedule and control projects. Thus, in the context of sustainable project scheduling context, two important elements are to be considered as decision variables: the input elements of a scheduling (e.g.

Scalable Air Traffic Management Simulation Environment

The Air Traffic Environment is changing, with many exciting concepts being introduced across the globe. The group developed an efficient simulation environment for this purpose. The objective of this project is to design and implement a new simulation environment with new features. Because of confidentiality, these features won’t be discussed here, but they represent challenges that need to be overcome.

Bioinspired Perching and Crawling Drones

Unmanned Aerial Systems (UAS) are the fastest growing sector of aviation with wide-ranging applications. Apart from flight there exists an urgent necessity for multi-role aerial robots. In spite of the rapid advancement in drone technology, there exist significant limitations including their inability to perform multiple roles.

Machine learning for quantum estimation and control

This project aims to develop effective estimation and control methods using machine learning for quantum systems. Benchmarking and controlling quantum systems have been an important task in next generation technology. However, efficient methods for the estimation and control of complex quantum systems are lacking.

High-bandwidth control for unmanned helicopters

This PhD work is a part of the project to develop high bandwidth control methods and advanced dynamic modelling for Rotorcraft Unmanned Aerial Vehicles (RUAVs). This will enable new roles such as the precision landing of RUAVs to the moving deck of a ship in rough seas. This and numerous other potential RUAV tasks are presently limited by the simple controllers used for such a responsive dynamic system.

Mechanical Tests of Quantum Theory

Quantum mechanics was developed as a theory of atomic behaviour. It was soon applied to light, assemblies of atoms, and subatomic particles. More recently, electrical circuits and mechanical systems operating in the quantum regime have been realized. This raises the question as to what, if any, are the ultimate limits to the application of quantum mechanics to physical systems.

Learning control of renewable energy systems

Renewable energy has shown promising future of practical applications. Advanced learning and control technology will enable efficient renewable energy systems. This project will explore learning control technologies to enhance the efficiency in renewable energy systems.

Research in Decentralized and distributed robust control

Interconnected control systems are widespread in engineering, defence and communications applications. Examples of such systems include a team of unmanned aerial vehicles pursuing a set of coordinated objectives, a platoon of vehicles on the highway, an array of actuated micro-electromechanical systems (MEMS), to name a few.