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.

Real-time Distributed Lifelong Optimisation Algorithms for Swarm Intelligence

A swarm is a group of decentralized decision nodes that need to coordinate and synchronise actions to achieve an effect. One example is in computer Ad-hoc networks where the nodes need to swarm to maximise network throughput. This project aims at designing and proving optimality conditions for new optimisation algorithms.

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.

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.

Signal Processing Techniques for Space Radar Applications

The Southern Hemisphere radar stations in Australia (Tidbinbilla, Parkes and Narrabri) provide a good platform to detect asteroids and study their trajectories and properties. Based on real measurements obtained through these stations, the project aims at providing signal processing tools to analyse planetary radar signals of near-earth objects, i.e., asteroids.

Evolution of water-jets from the centrebow and demihull prior to wetdeck slamming event

Over the past three decades there has been increased military and commercial interest in lightweight high-speed ships, mainly due to their ability to provide fast sea transportation and relatively high payload capacity. Australia is an acknowledged world leader in the innovative design and construction of large high-speed aluminium catamarans, such as the vessels developed by Incat Tasmania and Austal.

Leaching and recovery of elements from CCA timber

Copper chrome arsenic (CCA) timber is a major waste that is currently being stockpiled around Australia. The strict environmental restrictions on their disposal means that this stockpile will increase annually. This project looks at optimising the choice of acids for leaching of CCA from the timber, an understanding of the rate processes and chemical equilibrium that determine the extraction efficiency of those elements, and the recovery of the copper, chromium and arsenic from the waste stream.

Improving forecasts of space weather has been identified as a major goal for the space community. Space weather has substantial effects on space and ground operations, yet accurate forecast lead times are typically only a few hours to days at most. The goal of this PhD is to improve our understanding and forecasting capability of space weather through physics-based models and/or machine learning.

 

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.

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.

Design and Analysis of Efficient Coding Schemes for Internet of Things

The aim of this project is to research coding techniques for device to device cooperative communications in IoT. Specifically, this project will investigate techniques and methods to improve the throughput and efficiency of the cooperative communications in IoT.

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