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.

Achieving appreciable thrust for both cruise and accelerating flight in scramjet engines is only half the solution for a viable engine. Equally important, is the design and validation of a vehicle structure capable of withstanding the sustained high thermal loads, and the resulting thermally induced structural stresses.

Implementation of Direct Simulation Monte Carlo Algorithms using FPGAs

Direct Simulation Monte Carlo is a ubiquitous simulation method for low-density flows. It involves statistical simulation of representative populations of molecules in rarefied gas mixtures. The technique can simulate many physical processes very accurately at much less computational expense than direct molecular dynamic modelling.

Investigating Best Practices in Human-Systems Integration and Engineering Methodologies

This project aims at identifying current state of the art human systems integration techniques and creation of framework for an overarching or generalized human system integration framework that can be substantiated for different kinds of systems. Human systems integration practices are very domain specific for example healthcare, defence, aerospace etc.

Machine Learning based Real-time Scheduling

Integrated project planning and scheduling is a hot research topic that has provided a blueprint of project’s success that is based on uninterrupted completion of a project. However, in real production, the project environment changes dynamically because of external and internal fluctuations which create interruptions to the process, due to machine breakdowns, sudden material shortage and so on. These disturbances will mean that the optimal process plan and schedule may become less efficient or even infeasible.

Network Coding for Ultra-Low Latency Communications

The aim of this project is to research network coding techniques for ultra-low latency communication systems. Specifically, this project will investigate techniques and methods to improve the latency performance and efficiency of future low-latency wireless communication systems.

Deep Learning and Neural Networks for Multi-modal Human-Swarm Data Fusion

A human-swarm interaction generates very large real-time data streams including image, voice, EEG, human physiological data, task and swarm data, and interaction data. In our paper below, we took steps towards fusing two sources of data, images and timeseries/signal sensorial data. This project will need to systematically develop deep learning and neural network models and architectures to design general deep networks for multi-modal data fusion.

Vibrational nonequilibrium in nozzle flow

Gasdynamic lasers create optical gain from the rapid expansion of gas through a nozzle.  This can produce very high peak laser powers, and makes such lasers potentially useful in a range of applications.  The efficiency of the laser is determined by the ability to create and maintain a nonequilibrium distribution of vibrational energy within such a nozzle.  One effective way of driving the expanding flow is by providing elevated energy with combustion, however, some product species such as water vapour can impair the effectiveness of the rapid flow expansion in creating a population inversi

Performance of axial flow hydrocyclones

Axial flow hydrocyclones have both exits in the same direction unlike the reverse flow hydrocyclones that are commonly used in industry. Early work has shown that axial flow hydrocyclones can reduce pressure drop and the challenge is to optimise the design of the vortex finder and the outlets to improve the separation efficiency so that the axial flow hydrocyclones can be used to separate a wide range of materials including coal, minerals, and waste effluent.

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