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The following list provides information on projects currently open to new research students. For more information on each project please contact the nominated supervisor within the project description.

This page will be updated as opportunities become available so please check back regularly.

Centre for Ecosystem Management

Project Outline:
One of the most hotly debated topics in population and conservation biology is what is the best strategy for restoring small and endangered populations. When population augmentation (assisted migration and genetic rescue) is required, there are many open questions around the optimal genetic composition of source material to minimise the risks of outbreeding depression yet maximize evolutionary potential. Despite its importance, there is currently a lack of theoretical and experimental work to inform on-ground restoration practices. By combining new theoretical modelling of polygenic adaptation, with reciprocal transplants and experimental evolution, we will answer a number of outstanding questions including: (1) what is the optimal genetic composition of introduced material to ensure short and long term survival? (2) How does this change with trait genetic architecture and environmental fluctuations? This project is an international effort involving researchers from IST Austria (Nick Barton, Melinda Pickup, Himani Sachdeva). The applicant will be embedded within this established and multidisciplinary team and be a member of the internationally recognized Centre for Ecosystem Management in the School of Science.

Desired Skills: Very strong quantitative and programming skills (R or Python). Previous experience in population genetics and/or quantitative genetics.

Project Area: Evolution and ecology, population genetics, conservation biology

Supervisor(s): Dr David Field (contact A/Prof Ute Mueller)

Project level: Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 2, 2019

Project Outline:
The separation of species requires the evolution of genetic barriers to gene exchange. Understanding this crucial process requires that we identify the genetic differences involved and how they affect patterns of gene flow along genomes. In recent years, whole genome scans for excess divergence (e.g. Fst) has become a common way to identify candidate genes involved in adaptation and reproductive isolation. However, it is still unclear what causes ‘genomic islands’ and whether differential gene flow is involved. This PhD project will exploit the power of hybrid zones to compare various methods for assessing genomic differentiation and geographic clines in the genetically tractable Antirrhinum (snapdragon) system native to the Spanish Pyrenees. We will also assess alternative demographic models of secondary contact at independent hybrid zones to understand how past history influences the signals of genetic barriers observed today. The Antirrhinum system, with several known barrier loci (colour genes) provides a great opportunity to study the effects of localized barriers to gene flow on linked neutral variation along genomes. The research project is part of an Austrian Science Fund (FWF) awarded project (David Field – Chief Investigator) and part of an international effort involving researchers from IST Austria (Nick Barton), John Innes Centre UK (Enrico Coen) and University Paul Sabatier in Toulouse (Christophe Andalo). The applicant will be embedded within this established and multidisciplinary team with access to numerous genetic resources. Wet lab experience not necessary as most genomic data will be outsourced. All field work expenses and computational facilities covered.

Desired Skills: Good quantitative and programming skills (R or Python). Previous experience in population genetics and/or bioinformatics is desirable.

Project Area: Evolution, Population genomics, Bioinformatics

Supervisor(s): Dr David Field (contact A/Prof Ute Mueller)

Project level: Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 2, 2019

Project Outline:
The concept of the fitness landscape is an important unifying model in ecology and evolutionary biology, with relevance to a range of topics from the genetic architecture of reproductive isolation and local adaptation to niche diversification following adaptive radiations. Despite its importance, we know little about fitness landscapes in nature, due to the challenge of linking genotype, phenotype and fitness. This PhD project will contribute towards addressing this gap, using hybrid zones between Antirrhinum (snapdragon) species distinguished by divergent flower colours with a well-defined link from genotype to phenotype. Using whole genome sequencing, genome-wide association scans (GWAS) and detailed ecological field work in the Spanish Pyrenees, the project will (1) test whether fitness landscapes consist of deep valleys of low fitness how this fluctuates with the environment, and (2) identify the agents of selection and quantify whether this acts predominantly through male or female fitness. This research is funded by an Austrian Science Fund (FWF) awarded project (David Field – Chief Investigator) and part of an international effort involving researchers from IST Austria (Nick Barton), John Innes Centre UK (Enrico Coen) and University Paul Sabatier in Toulouse (Christophe Andalo). The applicant will be embedded within this established and multidisciplinary team with access to numerous genetic resources. Wet lab experience not necessary as most genomic data will be outsourced. Must be willing to work with the team at the field site in Spain (June-July). All field work expenses and computational facilities covered.

Desired Skills: Good quantitative and programming skills (R or Python). Previous experience in population genetics and/or quantitative genetics, field work with plants.

Project Area: Evolution and ecology, population genomics

Supervisor(s): Dr David Field (contact A/Prof Ute Mueller)

Project level: PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 2, 2019

Project Outline:
This project seeks to advance knowledge and theory on the new and poorly understood concept of loss and damage under climate change. It will achieve this by foregrounding the human geographies of people-place relationships in Western Australia. It will examine experienced and perceived threats from fires and droughts to sense of place and identity in order to locate loss, grief, and hope that matter to people and their lives. By starting with what urban, peri-urban, and rural residents around Perth value in their environments and deem worth protecting, the project’s intended outcome is a portfolio of acceptable and tolerable losses and locally-grounded  recovery strategies to inform policy on climate change responses. The project is a collaborative one between researchers at The University of Western Australia and Edith Cowan University.

Project Area: Environmental Sciences/Human Geography

Supervisor(s): Professor Pierre Horwitz

Project level: Honours, Masters

Funding: Funding available for Bursary and/or full field work expenses

Start date: Semester 2, 2018 or Semester 1, 2019

Security Research Institute

No results were found

eAgriculture Research Group

Project Outline:
The increased prevalence of outbreaks of mosquito borne diseases in developing countries such as India and Bangladesh and  regions of Australia has highlighted the need to find ways to predict these outbreaks across urban and regional areas.  A number of environmental and social and economic drivers can increase the prevalence of such outbreaks.

This project will investigate the use of geospatial technologies (location based systems) and AI  techniques to establish the occurrence of Dengue fever and Malaria disease outbreaks in Urban areas in India and Bangladesh.  The research aims to develop prediction models that can be used determine high priority areas for monitoring and prevention strategies.

Desired skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. R or other languages (Desirable)
  3. Experience in Medical or Biological Science (Desirable)
  4. Good oral and report writing skills (Essential)
  5. Able to work in team environment  (Essential)
  6. Good project management skills (Essential)
  7. Drivers License (Desirable)

Project Area: Computing and Medical

Supervisor(s):  Dr Leisa Armstrong, Dr Amiya Tripathy, Dr TM Shahriar

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1, 2018

Computing and Security

Project Outline:

Much effort in sustainable fisheries management is directed at estimating stock abundance. Catch and catch rates can provide some information, but inclusion of age structure information, of recreationally and commercially caught species, in stock assessment models is considered as the ‘gold standard’. To collect age data, growth rings are counted on the fish’s otolith (ear bone) in a manner similar to aging a tree by its growth rings. Aging of otoliths requires training readers on test sets of otoliths and comparing counts between reads and between readers Once trained, human readers must sit down and read hundreds if not thousands of otoliths for the species of interest. This is time consuming, costly and the repetitive nature of the task opens itself to errors through boredom and technique drift. Such a task however, is a perfect candidate for employing machine learning techniques in the development of an automated approach using images of previously collected and aged otoliths.

Hence, the aim of this project is to investigate and develop automated approaches incorporating deep learning for estimating otolith age of different species of fish. The proposed approach can be utilised in real world stock assessments, potentially providing reliable and consistent estimates and addressing limitations typically associated with human-centred methods.

Desired skills: Knowledge of Machine Learning; Experience in deep learning would be advantageous. Very strong programming skills

Project Area: Computer Vision, Image Processing, Deep Learning, Machine learning

Supervisor(s):  A/Prof. Chiou Peng Lam, A/Prof Martin Masek, Dr Rodney Duffy

Project level: Masters, PhD

Funding: Applicant to apply for ECUHDR or RTP Scholarship

Start date: Semester 2, 2020

Project Outline:

The now-commonplace ubiquitous computing and exponentially growing number of IoT networks urge steps toward the automation of digital forensic investigations. This is challenged by the ever-growing volume, variety, heterogeneity, and velocity of data related to, and generated by, computing devices. The ultimate goal of research in this field is to address some of these challenges, thereby overcoming the limitations of the state of the art.

Desired skills: Experience in digital forensics and artificial intelligence, programming skills

Project Area: Information Security

Supervisor(s):  Dr Leslie F Sikos

Project level: Masters, PhD

Funding: Applicant to apply for Cyber Security CRC scholarship

Start date: Semester 2, 2020

Project Outline:

This project aims to undertake PhD research in autonomous navigation of AI-powered ground vehicles in natural environments.
The project involves applying artificial intelligence and computer vision methods to enable light commercial ground vehicles to be used for autonomous navigation, mapping, scene analysis, and object recognition in natural environments.

Working with the supervisors, the successful candidate will contribute to the development of an accurate virtual environment that simulates the functionality and performance of candidate autonomous vehicles in various environmental terrains and interaction scenarios.

This includes developing geometric and dynamic models of the vehicles, exploring different sensors and sensors setups, and more importantly developing and testing AI algorithms for autonomous navigation and useful interaction with natural environments of interest. Simulation results will identify appropriate algorithms to be deployed onto operational and useful robotic platforms that can increase productivity and safety in real natural environments.

Desired skills:

Essential Skills:

  • BSc (Honours)/MSc/MPhil (with a significant research project component) in Computer Science, Mechatronics, Robotics, Electronics Engineering, Automation or a related subject.
  • Strong theoretical and applied knowledge in machine learning, as well as good programming skills in Python/ MATLAB/C++ or other languages of choice.
  • The successful candidate would be expected to have good oral and written communication skills.

Desirable Skills:

  • Ability and experience to operate light Unmanned Ground Vehicles (UGVs).
  • Experience of working with robot simulators such as CoppeliaSim and Gazebo.
  • Experience of working on robotics project(s) using Robot Operation System (ROS).
  • High-quality publication(s) in English.

Project Area: Artificial Intelligence and Autonomous Systems

Supervisor(s):  Prof David Suter and Dr Jumana Abu-Khalaf

Project level: PhD

Funding: VC Research Fellow HDR Scholarship (full time for 3 years with possible 6-month extension).

Start date: Semester 2, 2020

Contact for further information: To apply please send your expression of interest to Dr Jumana Abu-Khalaf (j.abukhalaf@ecu.edu.au). Put "Autonomous Navigation of AI-powered Ground Vehicles in Natural Environments - PhD Application" in the subject line.
Your expression of interest should include the following:

  • A cover letter that includes a brief statement of your suitability;
  • A brief CV containing your contact details; and
  • Academic transcript (s).

Science and Mathematics

Project Outline:

The concept of the fitness landscape is an important unifying model in ecology and evolutionary biology, with relevance to a range of topics from the genetic architecture of reproductive isolation and local adaptation to niche diversification following adaptive radiations. Despite its importance, we know little about fitness landscapes in nature, due to the challenge of linking genotype, phenotype and fitness. This PhD project will contribute towards addressing this gap, using hybrid zones between Antirrhinum (snapdragon) species distinguished by divergent flower colours with a well-defined link from genotype to phenotype. Using whole genome sequencing, genome-wide association scans (GWAS) and detailed ecological field work in the Spanish Pyrenees, the project will (1) test whether fitness landscapes consist of deep valleys of low fitness how this fluctuates with the environment, and (2) identify the agents of selection and quantify whether this acts predominantly through male or female fitness. The research project is part of an Austrian Science Fund (FWF) awarded project (David Field – Chief Investigator) and part of an international effort involving researchers from IST Austria (Nick Barton), Max Plank (Frank Chan) and John Innes Centre UK (Enrico Coen). The applicant will be embedded within this established and multidisciplinary team with access to numerous genetic resources. Wet lab experience not necessary as most genomic data will be outsourced. All field work expenses, research costs and computational facilities covered.

Desired skills: Good quantitative and programming skills (R or Python). Previous experience in population genetics and/or quantitative genetics.

Project Area: Evolution and ecology, population genetics and genomics

Supervisor(s):  Dr David Field

Project level: PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1, 2021

Project Outline:

The separation of species requires the evolution of genetic barriers to gene exchange. Understanding this crucial process requires that we identify the genetic differences involved and how they affect patterns of gene flow along genomes. In recent years, whole genome scans for excess divergence (e.g. Fst) has become a common way to identify candidate genes involved in adaptation and reproductive isolation. However, it is still unclear what causes ‘genomic islands’ and whether differential gene flow is involved. This PhD project will exploit the power of hybrid zones to compare various methods for assessing genomic differentiation and geographic clines in the genetically tractable Snapdragon system native to the Spanish Pyrenees and in Kangaroo Paws in Western Australia. We will also assess alternative demographic models of secondary contact at independent hybrid zones to understand how past history influences the signals of genetic barriers observed today. The research project is part of an Austrian Science Fund (FWF) awarded project (David Field – Chief Investigator) and part of an international effort involving researchers from IST Austria (Nick Barton), Max Plank (Frank Chan) and John Innes Centre UK (Enrico Coen). The applicant will be embedded within this established and multidisciplinary team with access to numerous genetic resources. Wet lab experience not necessary as most genomic data will be outsourced. All field work expenses, research costs and computational facilities covered.

Desired skills: Good quantitative and programming skills (R or Python). Previous experience in population genetics and/or bioinformatics is desirable.

Project Area: Evolution, Population genomics, Bioinformatics

Supervisor(s):  Dr David Field

Project level: Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1, 2021

Project Outline:

One of the most hotly debated topics in population and conservation biology is what is the best strategy for restoring small and endangered populations. When population augmentation (assisted migration and genetic rescue) is required, there are many open questions around the optimal genetic composition of source material to minimise the risks of outbreeding depression yet maximize evolutionary potential. Despite its importance, there is currently a lack of theoretical and experimental work to inform on-ground restoration practices. By combining new theoretical modelling of polygenic adaptation, with reciprocal transplants and experimental evolution, we will answer a number of outstanding questions including: (1) what is the optimal genetic composition of introduced material to ensure short and long term survival? (2) How does this change with trait genetic architecture and environmental fluctuations? This project is an international effort involving researchers from IST Austria (Nick Barton, Himani Sachdeva) University of East Anglia (Lewis Spurgin) and with local industry partners Greening Australia (Melinda Pickup).

Desired skills: Strong quantitative and programming skills (R or Python). Previous experience in population genetics and/or quantitative genetics.

Project Area: Evolution and ecology, population genetics, conservation biology

Supervisor(s):  Dr David Field

Project level: Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1, 2021

Project Outline:

The measurement of pH value is an important research topic in various fields like; water quality monitoring, blood pH level monitoring, clinical diagnosis and environment monitoring. The most commonly used method to sense pH is the conventional glass pH electrode. These glass electrode shows many advantages, such as; Nernstian sensitivity, long-term stability, high ion selectivity and wide operating range. However, they also have key disadvantages including, mechanical fragility, need for wet storage, large size, limited shape and high cost, which makes them impractical for some applications such as miniature pH sensor for capsule endoscopy and ambulatory esophageal pH monitoring.Therefore, various metal oxides have been investigated for use as pH sensors instead of the glass electrode, because of their insolubility, stability, mechanical strength and possibility of miniaturization. However, the main drawback of metal oxide pH sensors is interference caused by oxidizing and reducing agents in the sample solutions.

This project involves developing solid-state potentiometric pH sensors using metal oxides and nitrides, such as ruthenium metal oxide (RuO2) and titanium nitride (TiN) by manufacturing thin-films of these compounds using radio frequency magnetron sputtering using various conditions and then investigating the optimized thin-films for use as pH sensors.

If successful, the optimized material will be used to construct a solid-state pH sensor using an appropriate reference electrode and this sensor will potentially be able to use in lab-on-chips and pH sensor capsules which was a shortcoming for glass electrodes.

Desired skills: The research student is expected to possess a basic knowledge of materials science and electrochemistry with undergraduate studies and skills in chemistry, chemical engineering or physics.

Project Area: Materials science and electrochemistry

Supervisor(s):  Professor Kamal Alameh and Dr Magdalena Wajrak

Project level: Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1, 2021

Project Outline:

Public involvement in evaluation and decision-making processes such as environmental impact assessment (EIA) is a key component of practice. The purpose of this research is to evaluate a series of project development proposals in Western Australia to determine how influential public inputs are to approval decision outcomes. This research will establish an empirical evidence base of the effect of public involvement. Publishable outcomes are anticipated.

Desired skills: Sound research and writing abilities are desirable.

Project Area: Environmental impact assessment

Supervisor(s):  Professor Angus Morrison-Saunders

Project level: Honours, Masters

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: February 2021

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