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How to apply

For details on how to apply, please visit the NICTA Victorian Research Laboratory Scholarships page.

PhD projects

Postgraduate research opportunities exist in the areas outlined below.  Other research areas can also be created through consultation with the research leaders.  Please visit the CSP project pages for the research ares of CSP.

List of PhD projects available at the NICTA Victorian Research Laboratory:

Large Scale Systems

• Optimal flight-path for sensor registration: a certain region of airspace is under surveillance by a set of distributed sensors.  Air picture compilation by fusing distributed sensors requires the sensors to be properly registered.  Typical registration errors are azimuth bias, range bias, etc.  The sensors are assumed to be fixed and ground based, and can measure target range, azimuth range-rate, or any subset of these.  The measurement accuracy and the position of the sensors are assumed known.  The sensors are characterised by azimuth and range biases which have to be accurately estimated.  Sensor measurements are asynchronous and we consider a 2D space initially.  The problem is to determine the light path of an unmanned aerial vehicle (UAV) that will maximise the sensor bias estimation accuracy for a set of distributed sensors.  Please contact Nickens Okello: nokello<at>unimelb.edu.au.
• Modelling of soil-water-plant dynamics: Irrigation management is a complex matter in most modern agricultural enterprises.  The current practice of manual management is labour intensive and expensive.  To implement automation, considerable investment is required.  So typically, simulation studies are envisaged to predict the potential changes in behaviour, with the aim of deducing or predicting what the (economic) impact can be.  This requires that a good simulation model is available for the system under consideration.  Please contact Su Ki Ooi: suki@unimelb.edu.au. 
• Control of on-farm irrigation: Previous results show that automation leds to a substantial improvement in water saving and productivity compared to the existing manual operation in dairy pasture systems and apple orchards.  Thus, it is suggested that the application of real-time automation would dramatically improve economic water use while reducing water losses from the system leading to better environmental outcomes.  Therefore, the aim of this project is to further investigate and develop a smarter, fully automated on-farm irrigation, based on real-time measurement and control.  Please contact Su Ki Ooi: suki@unimelb.edu.au.

Life Sciences

• Using control theory tools in neuroprosthetics: electrical stimulation of neural tissue has been used to restore the function to visually impaired people and people with hearing loss, for treatment of epilepsy and Parkinson's disease.  A significant amount of time is spent on optimising stimulation parameters post-operatively.  Ideally, these parameters would be adjusted dynamically, based on the response of the stimulated neurons.  The aim of this project is to use control theory tools to find optimal stimulation parameters for successful neuroprosthetic stimulation.  Please contact Tania Kameneva: tkam<at>nicta.com.au.
• Modelling and characterisation of cell behaviour using time lapse microscopy: A range of projects are available analysing movies of cells and pathogens filmed both in vitro and in vivo.  Depending on the project, this may involve using techniques in computer vision, machine learning, optics or mathematical modelling.  These projects are interdisciplinary and would require significant engagement with the relevant wet lab.  Please contact John Markham: John.Markham<at>nicta.com.au.
  

Postgraduate research opportunities exist in the area of power systems modelling simulation and control.  This research will be conducted in conjunction with the Future Grid Centre at the University of Melbourne.  If you are interested, please get in touch with Mohammad Aldeen: aldeen<at>unimelb.edu.au.

• Power systems modelling simulation and control: Research programs are available in the area of power systems dynamics.  This line of research has been ongoing for a number of years now and the scope extension is still quite wide.  Previous research concentrated on dynamic stability and load-frequency control studies.  However, very little has been achieved in the special area of fault detection, especially in large scale interconnected power systems.  Our research in thei area involves fault modelling, simulation and detection and identification.  The next stage is to extend the results to nonlinear interconnected power systems and then incorporating fault detection and identification with control reconfiguration.  Dynamic, transient and voltage stability are natural candidates for such results.
• Smart grid: Research projects in the are of smart grid are available.  The projects involve assessments of how best integration of renewable energy sources like wind and solar can be achieved.  Issues like distributed generation, embedded generation and demand-side management are considered.
  

PhD topics available through the Australian National University in partnership with NICTA:

For an up-to-date list of available projects offered through the Australian National University in partnership with NICTA, please visit the following staff webpages:

• Dr. Adrian N. Bishop's webpage
• Prof. Brian D. O. Anderson's webpage
• Dr. Changbin Yu's webpage

PhD topics available through the University of NSW in partnership with NICTA:

• Distributed flow control in complex networks: research into distributed real time algorithms of flow control in complex networks.  This research includes analysis of structural vulnerability of complex networks and design of algorithms improving cascading failure tolerance of networks.  Power grids are studied as a typical example of real-world complex networks.  Supervisor: Prof. Andrey V. Savkin, Co-supervisor: Prof. Rob J. Evans.  Requirements/Prerequisites: The project call for PhD candidate with good degrees in related engineering or science areas and strong mathematical and analytical skills.  Please contact Prof. Andrey V. Savkin: a.savkin<at>unsw.edu.au.
• Distributed coverage control of wireless sensor networks: research into distributed real time algorithms for improving coverage control by wireless sensor networks.  The problems of barrier, sweep and blanket coverage of unknown regions will be studied.  New algorithms for distributed filtering with large-scale sensor networks will be designed.  Supervisor: Prof. Andrey V. Savkin, Co-supervisor: Prof. Rob J. Evans.  Requirements/Prerequisites: The project call for PhD candidates with good degrees in related engineering or science areas and strong mathematical and analytical skills.  Please contact Prof. Andrey V. Savkin: a.savkin<at>unsw.edu.au.

PhD topics available through the University of Sydney in partnership with NICTA:

The delivery of electricity is undergoing a fundamental change worldwide and is driven by the need for cheap, clean and reliable energy.  The increasing penetration of intermittent renewables in new and diverse ways means greater uncertainty in power available.  This introduces new system dynamics, and requires new mechanisms of load balancing, particularly in demand management and load control.  This requires a fundamental shift in how the system is designed and operated, and must directly consider how generators and consumers of different size and capabilities must interact.

Specifically, research topics are available in:

• Granulated estimation & control: Research into scalable distributed filtering and control algorithms capable of utilising the distributed computation and local communication between local devices such as smart meters and other monitoring and control devices within substations and regional control centres.
• Distributed control architecture: Research into design techniques for computation, communication and control architectures that optimally trade-off control performance, with communication cost, system complexity and infrastructure cost.
• Robust & resilient grid: Research into the balance between robust system design and online resilience, incorporating fast system reconfiguration, such that the system is able to withstand faults and hard to predict failures.
• Data-drive modelling & control: As significant amounts of data are gathered by new grid sensing systems and smart meters, there is an opportunity to exploit data-driven machine learning methods to allow better system models and associated uncertainties to be discovered and updated during operation.  This not only provides better models for other planning and control tasks, but may allow the best control responses to be optimised directly without the need to create intermediate and often overly simplistic models.

Please contact: George.Mathews<at>nicta.com.au, or David.Hill<at>sydney.edu.au

Other research areas can also be created in the general area of networked control through consultation with George Mathews.