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The following list provides information on projects currently open to new research candidates. 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.

Applied Analytical Chemistry

Project Outline:

The metabolome consists of endogenous metabolites, products from the microbiome, and exogenous chemicals arising from exposure (pharmaceuticals, toxins, etc.) and provides a highly sensitive measure of health and disease. Food-derived metabolites interact with genes, proteins, enzymes, and microbiota. This interaction modifies the function of cells, acts as a signal messenger, and regulates metabolic or energy pathways. The changes in metabolites induced by specific ingredients or foods along with the analysis of the corresponding metabolic fingerprint have great value, not only as a useful tool to evaluate their functionality in vivo, but also to establish a cause-effect link between the apparition of risk factors in diseases. This research project aims to understand the vascular health and corresponding metabolic impacts of particular plant foods and their bioactive components using a metabolomics approach.

Essential Skills: Ability to work in a team or independently and manage time across multiple demands whilst maintaining a high level of accuracy and productivity.

Project Area: Applied Chemistry

Research Centre: Centre for Integrative Metabolomics and Computational Biology (CiMCB)

Supervisor(s): Dr Armaghan Shafaei Darestani (SSCI), Prof David Broadhurst (SSCI), Dr Lauren Blekkenhorst (SMHS)

Project level: Masters / PhD

Funding: No external funding

Start date: Any

Project Outline:

This project involves the development of robust, accurate and reliable voltametric method for the detection of copper ions in copper standards using Online Voltammetric Analyser (OVA) with gold wire electrode and plementation of this method to the current Envirocart™ In-Water Hull Cleaning (IWHC) water treatment process during cleans after the removal of biofilm from commercial and Navy vessels.

Essential Skills: Completed major in chemistry major or equivalent

Desired skills: Laboratory experience using various chemical glassware, experience preparing accurately ppb and ppm concenrations of metal ion standards, experience using voltammetric instrument (Portable Digital Voltammeter, PDV6000+), pH meter, Excel and OneNote programs.

Project Area: Applied Chemistry

Supervisor(s):  Dr Magdalena Wajrak, Mr Paul Lewtas (ECU adjunct and ModernWater Company, UK), Mr Peter Maidment (CleanSubSea Company), Mr Brad Finch (B3 Company)

Project level: Honours / Masters

Funding: External funding maybe available for this project from CleanSubSea, Modern Water Company and B3 company or through Defence Science Centre (DSC) grant.

Start date: Semester 1, 2024

Project Outline:

Development and evaluation of novel teaching and learning resources such as; chemical demonstrations, computer simulations, videos of experiments, VR/AR animations and interactive quizzes, to assist chemistry students with understanding chemical concepts..

Essential Skills: Completed major in chemistry or Bachelor of Education with science major (chemistry) or equivalent

Desired skills: Laboratory experience using various chemical glassware, experience with videoing using eg GoPro device, experience with video and photo editing software, experience with developing VR/AR animations, advance knowledge of powerpoint and Canvas.

Project Area: Applied Chemistry

Supervisor(s):  Dr Magdalena Wajrak and Mr Tim Harrison (ChemLabS Teacher Fellow, Bristol University, UK)

Project level: Honours

Funding: This project will be funded through University and School Teaching and Learning grants and AAUT award funding.

Start date: Semester 1, 2024

Project Outline:

Measurement of pH is an important parameter in environmental, industrial and biological processes. A glass electrode is the most accurate instrument for pH measurement, however, it is brittle, susceptible to matrix effects and not possible for miniturisation.To overcome these issues of glass electrodes, various metal oxides and metal nitrides have been investigated and proposed as potential electrode materials as pH sensors. Therefore, this project involves development and study of various novel metal oxides and metal nitrides thin films as an alternative to glass pH electrode.

Essential Skills: Completed major in chemistry or physics and Honours in chemistry or physics or equivalent

Desired skills: Experience with potentiostat instrument, RF magnetron sputtering system, Scanning Electron Microsope (SEM), X-ray photoelectron spectroscopy (XPS), pH meter, Excel and OneNote programs.

Project Area: Applied Chemistry

Supervisor(s):  Dr Magdalena Wajrak and Prof Kamal Alameh (ECU adjunct professor)

Project level: Masters , PhD

Funding: External industry funding maybe be available for this project from Prof Alameh or from Rowe Scientific Scholarship.

Start date: Semester 1, 2024

Project Outline:

This project involves the development of Gas Chromatography (GC) methods using GC-methanizer for the detection of CO2, CO gases and short chained hydrocarbons, such as CH4 and C2H4 for various research applications such as; detection of products from elecrochemical reduction of carbon dioxide gas, detection of products of biomass and detection of CO2 and ethylene gas from fruit ripening in enclosed jars.

Essential Skills: Completed major in chemistry or equivalent

Desired skills: Experience with GC instrument, Chromeleon software, Excel and OneNote programs and statistical analysis.

Project Area: Applied Chemistry

Supervisor(s):  Dr Magdalena Wajrak

Project level: Honours, Masters

Funding: Funded by Rowe Scientific Scholarship.

Start date: Semester 1, 2024

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

Biological and Environmental Science

Project Outline:

Understanding connectivity across the urban environment is an important part of conservation management. This research project will combine genomic and environmental data to assess connectivity and genetic diversity in two fossorial skinks endemic to Western Australia — Buchanan’s snake-eyed skink, commonly found in urban gardens, and West-coast laterite ctenotus, restricted to remnant bushland. Comparing how these two different species move across the landscape will help inform conservation management of urban biodiversity. The student will conduct lab-based (DNA extractions) and computer-based (bioinformatics and population genomics analysis) work in this project. Field work is not required but may be optionally included.

Host University: ECU/Conservation and Biodiversity Research Centre (CBRC)

Desired skills: Prospective candidates should have a Bachelor of Science degree, preferably with specialisation in Conservation Biology, Wildlife Management or Genetics (or equivalent). Some field experience (reptile trapping, animal handling) desirable but not essential. Good interpersonal communication and the ability to work as part of a collaborative and multidisciplinary team will be expected from the candidate. The ability for independent, organised work and advanced communication skills in English (oral and written) are also essential.

Project Area: Conservation genetics / Landscape genetics / Genomics

Supervisor(s): Dr Sean Buckley (ECU), Dr Rob Davis (ECU), Dr Brenton von Takach (Curtin University) and Calum Irvine (UWA)

Project level: Honours/Masters

Funding: Internal funding for research expenses and sequencing

Start date: Semester 2, 2024 or Semester 1, 2025

Project Outline:

This project will focus on the Empodisma peat swamps in the Walpole Nornalup Wilderness, which recently gained national recognition as a Threated Ecological Community. These peat systems are globally unique but still little is known of their characteristics. To help improve their conservation a large, interdisciplinary team is undertaking a 5 year study to assess their biodiversity, hydrology and soils. Geophysics is one method being evaluated to characterise them. Resistivity and induced polarization geophysics have the potential to not only map the lateral and vertical distributions of peat but also to characterise the degree of organic matter decomposition. This project will include field work to undertake geophysical measurements at peat systems near Walpole and will collect samples to characterise the spatial distribution of soils, groundwater and carbon decomposition. There is the potential to continue this research as a post-graduate research project beyond 2024.

Host University: ECU/Department of Biodiversity, Conservation and Attractions

Desired skills: Prospective candidates should have Masters or Honours degree (first class or equivalent). Good interpersonal communication and the ability to work as part of a multidisciplinary team will be expected from the candidate. The ability for independent, organised work and advanced communication skills in English (oral and written) are also essential.

Project Area: Biological and Environmental Science

Supervisor(s): Dr David Blake (ECU), Dr Fabian Boesl (ECU) and Dr Gavan McGrath (DBCA)

Project level: PhD

Funding: Ian Potter Foundation

Start date: Semester 1, 2024

Project Outline:

Peatland systems are unique aquatic systems that support biodiverse flora and fauna populations. In the Walpole Wilderness Area of Western Australia, these peatlands are under threat from the impacts of climate change and in particular, fire. Fire can impact these systems through the drying, heating and combustion of organic-rich sediments. This can change the physical and through oxidation, the geochemical structure of the sediments resulting in high levels of acidity. This PhD will investigate what moisture thresholds prevent the combustion of these sediments, the influence of geochemistry on flammability, how the combustion of varying intensity influences the physical structure and geochemistry of these sediments and how this might influence the hydrology, hydrochemistry and subsequent flammability of these peatlands.   This exciting PhD will contribute to an essential knowledge base being documented over five years under the Ian Potter Foundation-funded PEAT project. Opportunities to work on peatlands in large multidisciplinary teams, and with Noongar Elders and Rangers, are available for the PhD candidate.

Host University: ECU / Department of Biodiversity, Conservation and Attractions

Desired skills: Prospective candidates should have Masters or Honours degree (first class or equivalent). Good interpersonal communication and the ability to work as part of a multidisciplinary team will be expected from the candidate. The ability for independent, organised work and advanced communication skills in English (oral and written) are also essential.

Project Area: Biological and Environmental Science

Supervisor(s): Dr David Blake (ECU), Dr Fabian Boesl (ECU) and Val Densmore (DBCA)

Project level: PhD

Funding: Ian Potter Foundation

Start date: Semester 1, 2024

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Project Area: Ecology

Research Centre: Centre for People, Place and Planet (CPPP)

Supervisor(s): Professor Pierre Horwitz and David Blake

Project level: Honours / Masters

Funding: External support will be sought from City of Wanneroo and City of Joondalup

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Project Area: Ecology

Research Centre: Centre for People, Place and Planet (CPPP)

Supervisor(s): Professor Pierre Horwitz and David Blake

Project level: Honours / Masters

Funding: Some external funding available for travel and equipment

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Project Area: Ecology

Research Centre: Centre for People, Place and Planet (CPPP)

Supervisor(s): Professor Pierre Horwitz (with Dr Catherine Baudains from Murdoch)

Project level: Honours / Masters

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Project Area: Ecology

Research Centre: Centre for People, Place and Planet (CPPP)

Supervisor(s): Professor Pierre Horwitz (collaboration with ECU Social Psychology team) (collaboration with ECU Social Psychology team)

Project level: Honours / Masters

Start date: Any

Project Outline: Several potential proejcts including climate change impacts, evolutionary biology and conservation

Project Area: Wildlife Conservation

Supervisor(s): Rob Davis

Project level: Honours / Masters /PhD

Funding: No external funding at this stage.

Start date: Any

Project Outline: Collaboration with Yarra Yarra Catchment Management Group & Gunduwa Conservation Regional Conservation Association.

Project Area: Ecology

Supervisor(s): Rob Davis, Dr Eddie van Etten,

Project level: Honours / Masters

Funding: Some funding available from collaborators

Start date: Any

Project Outline: With urbanisation, many remnants of Banksia woodlands (the largest ecosystem type in the Perth metro region) have been created ranging in size from <1 ha to several hundreds of hectares. This project explores the impacts of urban fragmentation on the important plant species of this ecosystem, from reproductive output and success, to fitness and other genetic consequences.

Project Area: Ecology

Supervisor(s): Dr Eddie van Etten, Rob Davis and David Field

Project level: Honours / Masters

Funding: Some funding for field work and genetic analysis.

Start date: Any

Project Outline: A considerable proportion of arid rangelands of Western Australia have been destocked in recent years following many decades of sheep/cattle grazing, mostly for economic reasons.  It is often assumed that the soils and vegetation will recover following cessation of livestock grazing, but this may not be the case if the land has been excessively degraded and/or grazing pressure continues via native and feral herbivores. This project, which has support from various government agencies, will address this issue across a range of scales from regional (using remote sensing) to local (field-based monitoring).

Project Area: Ecology

Supervisor(s): Dr Eddie van Etten, and others as required

Project level: Honours / Masters /PhD

Funding: In collaboration with Bush Heritage Australia and Rangeland NRM. Good chance of some funding.

Start date: Any

Project Outline: Wattles (Acacia spp.) from eastern Australia have invaded many areas of native forests throughout south-west Western Australia. This project will explore the patterns of invasion and the processes which facilitate invasion of these species, with a focus on directing action to best control or manage these species.

Project Area: Ecology

Supervisor(s): Dr Eddie van Etten, and Emeritus Professor Will Stock

Project level: Honours / Masters

Funding: No external funding

Start date: Any

Project Outline: A combination of 'new' and 'old' techniques will be used to explore, map and analyse the fire history of this large arid region of Western Australia.  Several government agencies will be involved.

Project Area: Ecology

Supervisor(s): Dr Eddie van Etten, Dr David Blake

Project level: Honours / Masters

Funding: In collaboration with Rangeland NRM, Dept of Agriculture WA and other agencies.

Start date: Any

Project Outline:

This project is best suited to someone with a strong birdwatching interest. It will quantify the contribution of a small number of honeyeaters to plant pollination and eventual reproductive success in the megadiverse northern sandplains region of WA. The project will involve structured bird surveys and mist-netting to undertake pollen swabs as well as targeted vegetation surveys.

Essential Skills: Interest and knowledge on birds, field skills and birdwatching capabilities

Project Area: Wildlife Conservation

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Rob Davis, and Dr Ryan Phillips, La Trobe Uni

Project level: PhD

Funding: Some external funding possible

Start date: Any

Project Outline:

An exciting opportunity exists to be part of a first ever experimental translocation of the Yellow-rumped Thornbill to Kings Park. This species has declined in the park and is a useful surrogate for other future translocations. Working in partnership with DBCA and Kings Park.

Essential Skills: Interest and knowledge on birds, field skills and birdwatching capabilities

Project Area: Wildlife Conservation

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Rob Davis

Project level: PhD

Funding: Support for Kings Park

Start date: Sem 2, 2022

Project Outline:

The Spectacled Hare-wallaby is the last surviving mainland species of hare-wallaby. It remains poorly known and there is concern it may be declining in the Pilbara. This project will work with curator of mammals Dr Kenny Travouillon at the WA Museum to assess the current conservation status and taxonomy of the Spectacled Hare-wallaby in WA.

Essential Skills: Interest in museum work and field surveys

Project Area: Wildlife Conservation

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Rob Davis, Dr Harriet Mills , Dr Kenny Travouillon (WA Musuem)

Project level: Masters / PhD

Funding: No external funding at this stage.

Start date: Any

Project Outline:

The population of Barking Owls in south-western Australia is perilously rare and poorly known. This project will conduct an Australia-wide study to determine the taxonomic distinctiveness of the SW WA population. It is an exciting opportunity for a passionate genetics student and will work with renowned Australian bird taxonomist Leo Joseph from the CSIRO National Wildlife Collection in Canberra.

Essential Skills: Genetic and laboratory skills, interest in taxonomy

Project Area: Wildlife Conservation

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Rob Davis, Dr Leo Jospeph (CSIRO)

Project level: PhD

Funding: Some support for genetics analyses

Start date: Any

Project Outline:

The Sunset Frog is a Vulnerable species restricted to peat swamps in Walpole, It is hihgly vulnerable to changes in fire management and the imapcts of climate change. This proejct suitbale for a masters or honours is an exciting opportunity to work on a threatened frog and will utilise analyses of automatic call recordings as well as on-ground surveys. Collaboration with DBCA Warren District.

Essential Skills: Fieldwork skills including ability to work at night

Project Area: Wildlife Conservation

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Rob Davis

Project level: Masters / PhD

Funding: No external funding at this stage.

Start date: Any

Project Outline:

Collaboration with Yarra Yarra Catchment Management Group.

Essential Skills: Fieldwork skills and interest in rural landscapes

Project Area: Ecology

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Rob Davis & Eddie van Etten

Project level: Honours / Masters

Funding: Potential funding via State NRM and other sources

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Research Centre: Centre for People, Place and Planet (CPPP)

Supervisor(s): Prof Angus Morrison-Saunders

Project level: Honours / Masters

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Research Centre: Centre for People, Place and Planet (CPPP)

Supervisor(s): Prof Angus Morrison-Saunders

Project level: Honours / Masters

Start date: Any

Project Outline:

Remnant bushland is of high conservation value, particularly within urban environments but it is within these environments that plant health is most at risk Monitoring the health of remnant bushland areas is resource intensive. High spatial resolution UAV data provides a mechanism to determine plant health, at the sub canopy scale. This project will focus on the use of UAV technology to determine a number of plant physiology metrics which will inform management practices within an A clss reserve within Perth.

Project Area: Plant Physiology/Remote Sensing

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s):  Dr David Blake, Dr Jason Stevens (DBCA - Kings Park) and Steve Easton (DBCA - Kings Park)

Project level: Masters /PhD

Funding: In-kind support DBCA (field travel and resources),possible funding/scholarship (DBCA)

Project Outline:

The peatlands in southwestern Australia, currently being listed as threatened ecological communites, are becoming increasingly vulnerable to fire. The direct impacts from fire are related to the severity of the combustion which is intrinsically linked to wetland sediment hydrology. This project looks to understand how sediment moisture levels influence the propogation of smouldering combustion throughout the sediment profile.

Project Area: Fire Ecology

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s):  Dr David Blake, Dr Valerie Densmore (DBCA)

Project level: Masters

Funding: In-kind support DBCA (field travel and resources)

Project Outline:

Project will evaluate the use of thermal infrared imaging captured with UAV to detect and differentiate mammal species in the wild. Field surveys will be conducted in south and mid-west WA.

Project Area: Wildlife Ecology/Remote Sensing

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s):  Dr David Blake, Dr Cheryl Lohr (DBCA)

Project level: Honours

Funding: In-kind support DBCA (field travel and resources)

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Project Area: Terrestrial Ecology

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Anna Hopkins, Dr Josephine Hyde (DBCA)

Project level: Honours / Masters

Funding: Funding and collaboration with DBCA

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Project Area: Ecology

Research Centre: Centre for People, Place and Planet (CPPP)

Supervisor(s): Dr Anna Hopkins, Dr David Blake, Professor Pierre Horwitz

Project level: Honours / Masters

Funding: Collaboration with farm industry groups, DPIRD

Start date: Any

Project Outline:

Interested applicants should contact one of the ECU supervisors mentioned below for further details.

Project Area: Fungal Ecology

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Anna Hopkins, Dr Katinka Ruthrof (DBCA)

Project level: Honours / Masters

Funding: Funding and collaboration with DBCA

Start date: Any

Project Outline:

This project will use eDNA and scat analysis to understand the specific dietary requirements of the rare and endangered Gilbert's Potoroo and develop eDNA survey methods to find new suitable locations for reintroduction programmes.

Project Area: Biological Science

Research Centre: Conservation and Biodiversity Research Centre (CBRC)

Supervisor(s): Dr Anna Hopkins, Dr Harriet Mills, Dr Jackie Courney (GPAG)

Project level: Masters / PhD

Funding: Collaboration with Gilbert's Potoroo Action Group

Start date: Any

Computer Science

Project Outline:

The peatlands that occur throughout southwestern Australia, including those in the Walpole Wilderness Area, are unique ecosystems at threat from climate change and in particular, fire. Soil moisture levels within peats are integral to maintaining the hydrological integrity of these systems and their resilience to fire. However, the hydrology of these systems is poorly understood. Significant improvements in remote sensing technologies mean that we now can estimate temporal fluctuations in soil moisture over large spatial scales by combining various remote sensing products. Recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) especially the transformer-based deep learning models also allow the obtaining and processing of a large volume of images, texts and other types of data relevant to the vegetation and fire-severity classifications. This project will develop an efficient and robust machine-learning approach to the fusion of multimodal data that can be used for reliable and automated monitoring of different biodiversity and geo-diversity aspects of the peatlands.

Desired skills: Prospective candidates should have Masters or Honours degree (first class or equivalent). Good interpersonal communication and the ability to work as part of a multidisciplinary team will be expected from the candidate. The ability for independent, organised work and advanced communication skills in English (oral and written) are also essential.

Project Area: Computer Science

Supervisor(s): Dr Shams Islam (ECU), Dr David Blake (ECU), Dr Fabian Boesl and Dr Mohammad Awrangjeb

Project level: PhD

Funding: Ian Potter Foundation

Start date: Semester 1, 2024

Project Outline:

Artificial intelligence (AI) is concretely reshaping our lives and it is time to understand its evolution and achievements to model future development strategies. This is true also for oncology and related fields, where AI is now opening new important opportunities for improving the management of cancer patients. The aim of this project is to investigate and develop an AI system to predict patients’ response to cancer treatment using real-world data.

Essential Skills: Excellent Python programming skills, basic understanding of machinemachine/deep learning concepts.

Project Area: Computing (AI)

Research Centre: Centre for AI and Machine Learning

Supervisor(s): Dr Afaq Shah

Project level: Honours / Masters

Funding: Students are encouraged to apply for ECU HDR and RTP scholarships.

Start date: Semester 2 2022

Project Outline:

In this project, we aim to develop machine learning techniques for automatic plant phenotyping using image/video data. Novel machine learning algorithms will be developed for measuring and estimating various traits of plants, e.g., length/shape of leaves, stem size, to name a few, using images/videos. We will also track the growth of plants over time using machine learning techniques and estimate their biomass. The project has practical applications and will have one of the supervisors from the agriculture sector.

Essential Skills: Excellent Python programming skills, basic understanding of machinemachine/deep learning concepts.

Project Area: Computing (AI)

Research Centre: Centre for AI and Machine Learning

Supervisor(s): Dr Afaq Shah, Associate Supervisor from DPIRD

Project level: Honours / Masters

Funding: students are encouraged to apply for CU HDR and RTP scholarships. The project will be completed in collaboration with DPIRD.

Start date: Semester 2 2022

Project Outline:

The main objective of this project is to develop AI system for the classification of facial images into different ethnic groups. Efficient and robust AI algorithms will be developed to achieve this objective.

Essential Skills: Excellent Python programming skills, basic understanding of machine/deep learning concepts

Project Area: Computing (AI)

Research Centre: Centre for AI and Machine Learning

Supervisor(s): Dr Afaq Shah

Project level: Honours / Masters

Funding: Some internal funding may be available, however, students are encouraged to apply for CU HDR and RTP scholarships

Start date: Semester 2 2022

Project Outline:

This research aims at developing a methodology that will allow for the automation of anomaly detection for cancer and fertility cell detection.

The research will investigate the optimal image processing techniques for detecting various attributes of these images.  The automation of this process can assist pathologists by reducing the time to analyze samples.

Current manual methods of  processing and detecting anomalies are both time consuming and require a degree of expertise .  The proposed outcome of this research will be the development of an automated expert system that can process through large numbers of pathology images and identify image anomalies.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. Image Processing techniques (Essential)
  3. Matlab, R or other languages (Essential)
  4. Experience in Medical science an advantage (Desirable)
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)

Project Area: Computer Science

Supervisor(s): Dr Leisa Armstrong and T M Shahriar, Dr Amiya Tripathy

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1 2022

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 and 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: eAgriculture Research Group and Centre for Environmental Management

Supervisor(s): Dr Leisa Armstrong and T M Shahriar, Dr Amiya Tripathy

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1 2021

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

Data Science, Statistics and Mathematical Modelling

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

Food Science and Agriculture

Project Outline:

The aims of this project is to develop Smart Decision Support Tool for Crop Nitrogen Management in Swan Coastal Plain using Artificial Intelligence and other related techniques

The project will carry out data collection and analysis and the development of the prototype Decision Support System (DSS) Tool.

  • Data set collection and pre-processing to develop a data repository
  • Preliminary data visualisation.
  • Assess the best classification techniques to establish the crop x nitrogen yield prediction models and effects under different climate and fertiliser scenarios
  • Validation with field data measurements to develop prediction model
  • Develop a prototype web application of the DSS tool.

This research is based existing methodologies on determination of nitrogen deficiency in wheat and can have a wide application to a range of crops grown in the Swan Coastal Plain.

This project will also use NDVI to model the relationship between nitrogen fertiliser management and crop growth, These techniques have already been shown to be useful for understanding watershed management (and vegetation degradation.  Other techniques such as data and geospatial visualization will also be explored.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  Essential
  2. R and other languages Desirable
  3. Skills in Computer Hardware and sensors  Desirable
  4. Experience in Plant Science, Agriculture/Horticulture an advantage Desirable
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)

Project Area: Computing and Agriculture (Digital Agriculture Research Group)

Supervisor(s): Dr Leisa Armstrong

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1 2022

Project Outline:

This project investigates analysis techniques that can be used to estimate changes in forest and agricultural productivity for different regions of Western Australia and Thailand

Environmental parameters such as NDVI and other vegetation indices have been used to estimate plant growth and production. This project will examine historical changes in these patterns and develop models to predict the effects of changing climate indices on the sustainability of these remnant vegetation areas and associated agricultural land.  This could provide a valuable tool to predict changes in flora and fauna in remnant vegetation in agricultural regions and the sustainability of current or future agricultural systems.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  Essential
  2. R and other languages Desirable
  3. Experience in Plant Science, Agriculture/Horticulture an advantage Desirable
  4. Good oral and report writing skills (Essential)
  5. Able to work in team environment  (Essential)
  6. Good project management skills (Essential)

Project Area: Digital Agriculture Research Group and Centre for Environmental Management.

Supervisor(s): Dr Leisa Armstrong and Dr Watinee Thavorntam

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1 2021

Project Outline:

This project examines the temporal and spatial variability of watersheds to determine the influence of water shed structures  and other landscape characteristics on the productivity of agricultural land within the catchment.

The research aims to develop methodology to collate, visualize and analyze complex climate, soils, land and agricultural production data from these watersheds.

This project follows on from earlier work undertaken in watershed in Andhra Pradesh State of India. The research aims to develop decision support tools that can be used to determine best practice watershed and catchment management.  The use of geospatial techniques and  computer science techniques will be used to develop an optimized model for different management scenarios .

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. R and other languages (Desirable)
  3. GIS technologies ArcGIS  (Essential)
  4. Experience in Plant Science, Agriculture/Horticulture an advantage (Desirable)
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)
  8. Drivers license

Project Area: Digital Agriculture  Research Group and Centre for Ecosystem Management

Supervisor(s): Dr Leisa Armstrong, Dr Dean Diepeveen and Dr Amiya Tripathy

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 2 2021

Project Outline:

This project will examine how expert based systems can be used to better manage fertiliser and irrigation regimes for a variety of crops grown in a vertical farming based systems.

The project will investigate techniques for monitoring and assessing crop status and use machine learning based techniques to develop optimized model for management of different crops.

The project will use a variety of methods to assess crop production through machine vision/image processing and analysis of plant growth under different environmental conditions.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  Essential
  2. R and other languages Desirable
  3. Skills in Computer Hardware and sensors  Desirable
  4. Experience in Plant Science, Agriculture/Horticulture an advantage Desirable
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)

Project Area: Computing and Agriculture (Digital Agriculture Research Group)

Supervisor(s): Dr Leisa Armstrong, Dr David Cook

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1 2021

Project Outline:

This project will examine specific image processing techniques that can be used to assessing frost and nutrient damage of broad acre crops using drone and other images.

The project will investigate the issues associated with integrating image processing into nutrient modeling and the influences of crop nutrition on predicting likelihood of susceptibility to leaf and head frost damage.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. Image processing (Essential)
  3. R or  other languages (Essential)
  4. Experience in Plant Science, Agriculture/Horticulture an advantage (Desirable)
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)
  8. Drivers license (desirable)

Project Area: Computing and Agriculture (Digital Agriculture Research Group)

Supervisor(s): Dr Leisa Armstrong, Dr David Cook, Professor Dean Diepeveen

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1 2022

Project Outline:

This research looks at the use of geospatial technologies to investigate crop growth production at paddock, district and regional levels.The project will use In-season monitoring of crop growth using UAVs and geospatial techniques including video and sensor technologies to understand crop production systems

The project will use:

  • Techniques such as image processing will be used determine changes in the crop production.
  • Ndvi images and ground truthing will also be used to validate.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. R and other languages (Desirable)
  3. GIS technologies ArcGIS  (Essential)
  4. Experience in Plant Science, Agriculture/Horticulture an advantage (Desirable)
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)
  8. Drivers license

Project Area: Computing and Agriculture (Digital Agriculture Research Group)

Supervisor(s): Dr Leisa Armstrong, Professor Dean Diepeveen and Dr Amiya Tripathy

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship and Honours Scholarship

Start date: Semester 1 2022

Project Outline:

  1. The partner for this project will be with Department of Primary Industries and Regional Development (DPIRD) and several Farmer Groups
  2. This project will look the use of images to map crop growth patterns across paddocks to determine changes in crop health (caused by poor nutrition herbicide damage, pest damage).
  3. The project will use In-season monitoring of crop growth using UAVs and geospatial techniques including video and sensor technologies to monitor crop health.
  4. The project will use
    1. Techniques such as image processing will be used determine changes in the crop damage;
    2. Ndvi images and ground truthing will also be used to validate
    3. These techniques will used to determine if they can be upscaled to predict patterns across the cropping regions of WA

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. R and other languages (Desirable)
  3. GIS technologies ArcGIS  (Essential)
  4. Experience in Plant Science, Agriculture/Horticulture an advantage (Desirable)
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)
  8. Drivers license

Project Area: Computing and Agriculture (Digital Agriculture Research Group)

Supervisor(s): Dr Leisa Armstrong, Professor Dean Diepeveen

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship and Honours Scholarship available

Start date: Semester 1 2022

Project Outline:

This project will be done in collaboration with Dr Dean Diepeveen, Department of Primary Industries and Regional Development (DPIRD)

The project will use various AI and geospatial techniques to interrogate the agriculture and climate data sets in WA cropping regions.  A prototype extreme weather prediction model will be developed that will use the AI and Geospatial techniques with crop models and incorporate seasonal climatic data to predict distribution of frost damage and other extreme weather damage.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. R and other languages (Desirable)
  3. GIS technologies ArcGIS  (Essential)
  4. Experience in Plant Science, Agriculture/Horticulture an advantage (Desirable)
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)
  8. Drivers license

Project Area: Computing and Agriculture (Digital Agriculture Research Group)

Supervisor(s): Dr Leisa Armstrong, Professor Dean Diepeveen

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship and Honours Scholarship available

Start date: Semester 1 2022

Project Outline:

This project will investigate technologies to improve Urban Farming systems. Decision support systems will be developed to monitor crop production and climate in vertical farming systems. This will be used improve leaf growth and resource allocation (fertilizer, solar radiation, temperature) in these systems.

The research will develop a pilot test bed to monitor crop growth using low cost sensor networks and will develop a prototype monitoring system for detecting real time assessments of crop growth. Data sets collected will be used to develop a cropping model for the application of nutrients and water for crop growth. CS techniques such as Bayesian networks will be employed to optimize the application of nutrients.

Desired Skills:

  1. Skills in Database Programming, and Artificial Intelligent systems and Advanced Computer Programming.  (Essential)
  2. R and other languages (Desirable)
  3. Skills in Computer Hardware and sensors  (Desirable)
  4. Experience in Plant Science, Agriculture/Horticulture an advantage (Desirable)
  5. Good oral and report writing skills (Essential)
  6. Able to work in team environment  (Essential)
  7. Good project management skills (Essential)

Project Area: Computing and Agriculture (Digital Agriculture Research Group)

Supervisor(s): Dr Leisa Armstrong, Professor Dean Diepeveen, Dr David Cook

Project level: Honours, Masters, PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Semester 1 2022

Security Science

Project Outline:

As wireless communications become ubiquitous in our daily life, the emerging illegal use of the radio frequency (e.g., jamming, malicious interference, covert transmission) is causing increasing privacy and security concerns. Against this background, the detection of the malicious signals in the context of wireless networks is of growing importance. In such detection, the radio frequency (RF) noise power plays a critical rule, since noise widely exists in a RF system even when an information signal does not exist. This project is on collecting and modelling the RF noise power. The first task of this project is to conduct a thorough literature review on the available RF noise power dataset and models, understanding the noise theory, methods of measuring RF noise power, and the noise power uncertainty source. The second task of this project is to collect such a dataset via using the available platforms at ECU (e.g., Ettus X310, N210, Spectrum analyzer). Then, the final task is to use the established dataset in one application scenario, such as malicious interference detection, GPS spoofing detection, or covert signal transmission, in which new and novel model should be developed. This project should lead to multiple high-quality journal publications.

Literature samples:

Available platforms at ECU:

Preferred Skills (not required): Experience with software define radios, MATLAB, basic wireless communication knowledge.

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao YAN

Project level: Masters / PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Any

Project Outline:

As drones are becoming ubiquitous in our daily life, privacy and security concerns caused by them are of ever increasing. For example, they could be illegally used for spying or flying in protected regions (e.g., nearby airports). Therefore, drone detection is on a high demand. This project focuses on drone detection based on radio frequency (RF) used in their wireless communications with the controllers or ground stations. The first task of this project is to conduct a thorough literature review on different RF drone detection strategies, identifying the communication frequency, handshake process, and handover stages. The review should clarify the difference between drone RF and other RF (e.g., WiFi) signals. In addition, the frequency hopping may be used in drone communications and thus communication channel tracking should be also examined in this task. The second task of this project is to test various RF drone detection schemes on the available drone systems (e.g., DJI Matrice 300 RTK, DJI RoboMaster TT Starter) and software defined radios (SDRs) or spectrum analyzer. The last task is to develop new and novel RD drone detection schemes, of which the performance should be analyzed and tested. This project should lead to multiple high-quality journal publications.

Literature samples:

Available platforms at ECU:

Preferred Skills (not required): Experience with software define radios, MATLAB, basic wireless communication knowledge.

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao YAN

Project level: Masters / PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Any

Project Outline:

As drones are becoming ubiquitous in our daily life, privacy and security concerns caused by them are of ever increasing. For example, they could be illegally used for spying or flying in protected regions (e.g., nearby airports). Therefore, anti-drone system is on a high demand, which aims at bringing off the drones. This project focuses on anti-drone or counter-drone systems based on radio frequency (RF) used in their wireless communications with the controllers. The first task of this project is to conduct a thorough literature review on different RF anti-drone systems, identifying the communication frequency, transmit power, and RF signal transmission direction. The review should clarify the potential interference and risks of using such RF jamming signals in various scenarios. In addition, the frequency hopping may be used in drone communications and thus communication channel tracking and then jamming should be also considered in this task. The second task of this project is to test various RF anti-drone schemes on the available drone systems (e.g., DJI Matrice 300 RTK, DJI RoboMaster TT Starter) and software defined radios (SDRs) or spectrum analyzer. The final task is to develop new and novel anti-drone systems based on RF jamming. This project should lead to multiple high-quality journal publications.

Literature samples:

Available platforms at ECU:

Preferred Skills (not required): Experience with software define radios, MATLAB, basic wireless communication knowledge.

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao YAN

Project level: Masters / PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Any

Project Outline:

Physical layer key generation has been emerged as an alternative key sharing solution relative to the public key cryptography systems. It is based on the wireless channel reciprocity, which enables that the two transceivers share some common randomness that is unknown to others and thus can be explored to generate secret cryptographic keys. This project focuses on the physical layer key generation based on channel state information (CSI), which can potentially achieve higher key rates relative to that based on received signal strength. The first task of this project is to conduct a thorough literature review on the CSI-based key generation schemes, understanding the key generation steps, achievable key rate, and the limiting factors. The second task of this project is to build up a CSI-based physical layer key generation system via using the available software and hardware platforms at ECU (e.g., Ettus X310, N210, Spectrum analyzer). Then, the final task is to examine the impact of different system parameters (e.g., signal-to-noise ratio, channel correlation) on the achievable key rate and then develop new schemes to enhance the key rate. This project should lead to multiple high-quality journal publications.

Literature samples:

Available platforms at ECU:

Available software platforms:

Preferred Skills (not required): Experience with software define radios, MATLAB, basic wireless communication knowledge.

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao YAN

Project level: Masters / PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Any

Project Outline:

Radio frequency (RF) fingerprinting is able present unique characteristics (e.g., carrier frequency of the oscillators) of a wireless device, which can be used to authenticate this device. This device authentication based on RF fingerprinting has been emerged as an alternative of the traditional authentical based on public key cryptography, especially for the low-cost and low-complexity wireless devices in the context of Internet of Things. This authentication first requires a valid dataset of the RF fingerprinting. Therefore, the first task of this project is to conduct a thorough literature review on the existing RF fingerprinting dataset and the authentication schemes (e.g., based on machine learning), identifying the used unique characteristics or other features, authentication performance, and the limiting factors. The second task of this project is to collect a RF fingerprinting dataset via using the available software and hardware platforms at ECU (e.g., Ettus X310, N210, Spectrum analyzer). Then, the final task is to apply existing authentication schemes by using the existing and self-collected dataset, while new authentication schemes should be developed as well. The performance of various authentication schemes will be also examined and analyzed by using the existing and self-collected dataset in this task. This project should lead to multiple high-quality journal publications.

Literature samples:

Available platforms at ECU:

Available software platforms:

Preferred Skills (not required): Experience with software define radios, MATLAB, basic wireless communication knowledge.

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao YAN

Project level: Masters / PhD

Funding: Applicant should apply for ECUHDR or RTP Scholarship

Start date: Any

Project Outline:

This project will develop an innovative framework to resolve the critical and challenging problem of optimally controlling and exploiting random interference to achieve communication covertness, so as to enable large-scale covert communications. Meanwhile, optimal strategies of detecting covert communications will also be developed for establishing the fundamental performance limits on the achievable communication covertness. Specifically, this project will design a novel framework to tackle the optimal masking strategy via exploiting random interference to achieve covert communications, develop optimal detectors against such covert communications and establish technological foundations of the resulting communication covertness.

Essential Skills: Wireless communication or cybersecurity background

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao Yan

Project level: Honours / Masters / PhD

Start date: Any

Project Outline:

Research focus on the authentication of location information has increased over the past few years. This focus is in part due to the growing awareness of the continuously-increasing importance of location information and how vulnerable this information is to malfunctions (e.g., faulty GPS positions), malicious falsification and spoofing. Location authentication refers to the  verification of a position claimed by a device or user. This project aims to deliver the world's leading location authentication algorithms for emerging wireless networks. Specifically, the project will develop novel Artificial Intelligence (AI)-based algorithms for location authentication under real-world conditions.

Essential Skills: Wireless communication or cybersecurity background

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao Yan

Project level: Honours / Masters / PhD

Start date: Any

Project Outline:

This project aims to deliver innovative and effective techniques to enable covert communications for private and secure information transmission. The demand for security and privacy in wireless communications is ever increasing, especially as Internet-of-Everything (IoE), widely regarded as the next technology revolution, is coming to reality to create a connected world for sharing critical information. This project will develop new designs for integrating the highly compatible intelligent reflecting surface (IRS) technique into the novel covert communication framework developed in the previous aims, and identify the optimal balance between signal intensification and signal cancellation, as a critical step towards achieving high-rate IRS-aided covert wireless communications.

Essential Skills: Wireless communication or cybersecurity background

Project Area: Wireless Cybersecurity

Research Centre: Security Research Institute (SRI)

Supervisor(s): Dr Shihao Yan

Project level: Honours / Masters / PhD

Start date: Any

Marine Science

Project Outline:

Seed based restoration is a valued resilienece building strategy in seagrass ecosystems. This project will conduct field and / or aquarium experiments to improve understanding of the drivers of seed production, seed germination, establishment and survival.

Project Area: Ecology and environmental management

Research Centre: Centre for Marine Ecosystems Research (CMER)

Supervisor(s): Associate Professor Kathryn McMahon

Project level: Masters / PhD

Funding: External funding available

Start date: Any

Project Outline:

With rapidly changing climates there is a need to actively manage and build resilience into ecosystems. This project will assess using field and / or aquarium experiments the resilience of seagrass plants and seedlings to heatwaves.

Project Area: Ecology and environmental management

Research Centre: Centre for Marine Ecosystems Research (CMER)

Supervisor(s): Associate Professor Kathryn McMahon

Project level: Masters / PhD

Funding: External funding available

Start date: Any

Project Outline:

This projects would use gut content and stable isotope analyses to examine the food web structure and flow of carbon from base sources (plants) to higher order consumers in Cockburn Sound. This system has undergone major changes over the last 50 years and is under constant pressure from industry including port facilities. The aim of the study would be to determine key components of the food web that will inform future management of the system.

Research Centre: Centre for Marine Ecosystems Research (CMER)

Supervisor(s): Professor Glenn Hyndes

Project level: Honours / Masters

Start date: Any

Project Outline:

This projects would examine benthic invertebrate communities in Cockburn Sound to determine the dominant taxa and their abundances and compare to existing data from previous decades to understand what changes have occurred over time. This system has undergone major changes over the last 50 years and is under constant pressure from industry including port facilities. The study would  inform future management of the system.

Research Centre: Centre for Marine Ecosystems Research (CMER)

Supervisor(s): Associate Professor Kathryn McMahon

Project level: Honours / Masters

Start date: Any

Project Outline:

There are a variety of topics available in this theme including investigating how marine plants can cope with climate induced changes in temperature & salinity across different life cycle stages and what factors are critical for promoting resilience such as genetic diversity and connectivity.

Research Centre: Centre for Marine Ecosystems Research (CMER)

Supervisor(s): Associate Professor Kathryn McMahon

Project level: Honours / Masters

Funding: Funding and facilities available to support field work and aquarium experiments

Start date: Any

Project Outline:

There are a variety of topics available in this theme including investigating how marine plants can cope with climate induced changes in temperature & salinity across different life cycle stages and what factors are critical for promoting resilience such as genetic diversity and connectivity.

Project Area: Coastal Ecology

Research Centre: Centre for Marine Ecosystems Research (CMER)

Supervisor(s): Dr Sora M Estrella & Rob Davis

Project level: Honours / Masters

Funding: No external funding at this stage but project developed in collaboration with the LCC.

Start date: Any

Project Outline:

The Vasse-Wonnerup is a coastal wetland containing two estuaries, the Vasse and the Wonnerup rivers estuaries and their connecting channels. This wetland has been heavily modified, but despite this, it hosts tens of thousands of waterbirds each year and was recognised as a “Wetland of International Importance” under the Ramsar Convention in 1990. Several studies have been carried out in the system, with the final objective to improve its management. However, there is still a substantial lack in the knowledge of the trophic ecology and diet of the wetland’s waterbirds. The information obtain from this project will assist to understand waterbirds distribution and use of the wetland as well as inform future management.

Project Area: Coastal Ecology

Research Centre: Centre for Marine Ecosystems Research (CMER)

Supervisor(s): Sora M. Estrella, Glenn Hyndes & Chandra Salgado Kent

Project level: Honours / Masters

Funding: In-kind from DBCA, plus other potential funding from DWER and SWCC

Start date: Any

Physics

Project Outline:

This project will build on previous work on the development of photovoltaic power converters for power over fibre applications, including analysis of optical power attenuation in optical fibres used in these applications.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding

Start date: Any

Project Outline:

This project will examine the dynamic properties of tendons (using tendon phantoms) and develop new methods for experimentally determining tendon properties.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding

Start date: Any

Project Outline:

A range of projects are available in the use of fobre Bragg gratings (FBGs) to the measurement of different structural properties and their application to structural health monitoring and acoustic detection.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding

Start date: Any

Project Outline:

This project will examine the use of smartphones in physics lab experiments.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters

Funding: No external funding

Start date: Any

Project Outline:

This project will examine the use of infrared light (2 to 5 microns) for the detection of the initial ignition stages of forest fires.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding (at this stage).

Start date: Any

Project Outline:

In this project, acoustic signals will be used to monitor the health of trees.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding.

Start date: Any

Project Outline:

In this project, acoustic emission studies will be used to examine th quality of cricket bats.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding.

Start date: Any

Project Outline:

The aim of this project is to develop a low cost subterranean acoustic measurement system to detect and identify cane grubs in cane fields.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding.

Start date: Any

Project Outline:

This project will explore the physics of devices such as the spear thrower (woomera) or the boomerang.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding.

Start date: Any

Project Outline:

This project will explore the physics of operation of different musical devices that have been created by aboriginal culture. Different instruments can be examined, including the didgeridoo (yadiku), the tuntun (bullroarer) and the leaf (xxx).

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding.

Start date: Any

Project Outline:

This project will build on a previous student project and develop models of quantm well based solar cells, by simulating the output characteristics of the solar cells incorporating multiple layer quantum wells.

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters / PhD

Funding: No external funding.

Start date: Any

Project Outline:

This project extends a previous PhD project that involved the study of the concept of Power-Over-Fibre.

Supervisor(s): Associate Professor Steven Hinckley and Dr Steven Richardson

Project level: Honours / Masters

Funding: No external funding.

Start date: Any

Project Outline:

Image artefacts are present in all OCT images, irrespective of the configuration of the OCT measurement system. These artefacts can lead to misdiagnosis in real OCT images using in medical and opthalmatic examinations. This project will examine the generation of artefacts under specific interferometric conditions. Eventually this could lead to methods and algorithms of removing these artefacts in real-time during imaging

Supervisor(s): Associate Professor Steven Hinckley and Dr Steven Richardson

Project level: Honours / Masters / Phd

Funding: No external funding at present. There may be external funding in the future.

Start date: Any

Project Outline:

The project will compare the A-scans obtained from two different OCT models - a fundamental physics mode and the montecarlo method model - that have been developed by two different research groups at ECU. The primary goal is to compare the effects of scattering and absorption on A-scans, as implemented in the two models.

Project Area: Physics

Supervisor(s): Associate Professor Steven Hinckley and Dr Steven Richardson

Project level: Honours / Masters

Funding: No external funding at present. There may be external funding in the future.

Start date: Any

Project Outline:

This project will be performed in collaboration with industry practitioners who work in this field of medical research and implementation.

Project Area: Physics

Supervisor(s): Associate Professor Steven Hinckley and Dr Steven Richardson

Project level: Honours / Masters

Funding: No external funding at present. There may be external funding in the future.

Start date: Any

Project Outline:

This project will extend the results obtained under a previous research grant with the GRDC. It will attempt to characterise the acoustic absorption properties of different grains (or grain phantoms) inside a grain storage silo. Acoustic signature detection is one method of detecting, identifying and monitoring insects insde such structures. However, there is a need to identify whether the acoustic signature from the insect will actually pass through the grain system and be detected.

Project Area: Physics

Supervisor(s): Associate Professor Steven Hinckley

Project level: Honours / Masters

Funding: No external funding

Start date: Any

Proteomics

No results were found

Microbial ecology

Project Outline:

The peatlands that occur throughout southwestern Australia, including those in the Walpole Wilderness Area, are unique ecosystems at threat from climate change and in particular, fire. Soil moisture levels within peats are integral to maintaining the hydrological integrity of these systems and their resilience to fire. However, the hydrology of these systems is poorly understood. Significant improvements in remote sensing technologies mean that we now can estimate temporal fluctuations in soil moisture over large spatial scales by combining various remote sensing products. Recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) especially the transformer-based deep learning models also allow the obtaining and processing of a large volume of images, texts and other types of data relevant to the vegetation and fire-severity classifications. This project will develop an efficient and robust machine-learning approach to the fusion of multimodal data that can be used for reliable and automated monitoring of different biodiversity and geo-diversity aspects of the peatlands.

Desired skills: Prospective candidates should have Masters or Honours degree (first class or equivalent). Good interpersonal communication and the ability to work as part of a multidisciplinary team will be expected from the candidate. The ability for independent, organised work and advanced communication skills in English (oral and written) are also essential.

Project Area: Microbial ecology

Supervisor(s): Professor Elizabeth Watkin (ECU), Dr Quinton Burnham (ECU) and Dr Fabian Boesl

Project level: PhD

Funding: Ian Potter Foundation

Start date: Semester 1, 2024

Project outline: Most Proteaceae, such as banksias and grevilleas, produce specialised cluster roots that mine the soil for nutrients, especially phosphorus. This mining activity allows them to live on severely nutrient-impoverished soils and involves the release of large amounts of carboxylates, typically citric acid and malic acid. These carboxylates are a ready energy source for the soil microbial community. However, the impact of this source of energy on the microbial community and how this impact might be of benefit to the plant is unknown. Using molecular fingerprinting, this project will identify the differences in the prokaryotic community structure between the soil mined by cluster roots and the neighbouring soil not mined by cluster roots. Using database searches, the types of prokaryotes present will be determined, generating hypotheses about their potential impact on the plant.

Supervisors: Professor Elizabeth Watkin, Associate Professor Patrick Finnegan (School of Biological Sciences, UWA)

Contact: Professor Elizabeth Watkin

Level: Honours/Masters

Funding: Funding available for this project. Collaboration with UWA

Preferred start date: Semester 1 2023.

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