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Information and Communication Technology Research

We conduct research into topics across the breadth of the ICT spectrum. Our four strategic research themes are Smart Services and Systems; Games and Creative Technologies; Transformative Information Systems; and Human Interface Technologies.

Strategic investments and initiatives, such as the HIT Lab, help us enhance and improve our research scope and output. Our innovative platforms and research topics attract high performing researchers from around the world as well government and industry collaborations, which provide ongoing benefit both to our students and our researchers.

Our research themes

  Study with us

Every industry both within Australia and around the world needs ICT graduates, who can combine technical skills with business know-how. Our research degrees offer advanced technical skills and work integrated learning across information technology and systems, and design technology. They contain a large proportion of professional development, meaning graduates gain skills and knowledge ideal for taking an ICT career to the next level, or leveraging existing qualifications for a move into the ICT industry.

Available Research Degree Projects

A research degree candidate may develop their own research project in collaboration with their supervisors or apply for one of our currently available projects below:

Applicants interested in a specific project should first contact the supervisor listed and then find out more about our Entry Requirements, Scholarships if relevant, and then Apply Now.

Closing Date

1st December 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Facial Emotion Recognition is the process of identifying human emotion, most typically from human facial expressions. AI-based facial emotion detection can be applied in a variety of fields such as Driver Fatigue Monitoring, Marketing, and Entertainment. Driver Fatigue Monitoring employs facial emotion detection to determine whether a driver is in a state of fatigue so as to appropriately intervene in the behaviour of the driver to avoid possible accidents. Advertisers and market researchers try to use consumer emotional engagement with digital content, such as videos and ads, to create the best ads and optimizing media spend.

The popularity of deep learning approaches in the domain of emotion recognition may be mainly attributed to its success in related AI applications such as Computer Vision. Well-known deep learning algorithms include different architectures of Deep Neural Network (DNN) such as Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Extreme Learning Machine (ELM). Deep Neural Networks have increasingly been employed to learn discriminative representations for automatic facial emotion recognition with some success, however, certain significant issues remain unresolved. Such issues include: Occlusion-robust and pose-invariant issues; Dataset bias and imbalanced distribution; Optimal DNN parameter set; Multimodal effect. In this project, you will develop new deep learning algorithms to overcome these and possibly other issues for faster, more reliable, and more accurate facial emotion detection.

Eligibility

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Shuxiang Xu for further information.

Closing Date

10th February 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Machine learning approaches have been used for developing predictive models such as recommender systems, which seek to predict the preference that a user would give to an item. In recent years a new algorithm named Extreme Learning Machine (ELM) has been developed for training Artificial Neural Networks (ANNs). With ELM, there are no iterations for adjusting connection weights and parameters tuning as in back propagation based ANNs.

While ELM has demonstrated superior performance in developing smaller recommender systems, one drawback of it is that, given an application with a big dataset, the number of neurons in its single hidden layer are typically very large and hence training the network can be computationally impractical. The ELM algorithm’s complexity is at least O(KM2), where K is the number of training instances and M is the number of hidden units. ELM also makes use of batch training, which leads to large memory consumption.

The project aims to evaluate several different solutions (such as representation learning and Deep ELMs) for these problems, and propose a new algorithm for maintaining the strengths of ELM but overcoming its weaknesses in performance and efficiency. Such a solution would be very valuable for developing more effective recommender systems in the current big data era.

Eligibility

The following eligibility criteria apply to this project:

  • The project is open to domestic (Australian and New Zealand) and international candidates
  • The degree must be undertaken on a full-time basis
  • Applicants must already have been awarded a First Class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills

Candidates from a variety of disciplinary backgrounds are encouraged to apply. Knowledge and skills that will be ranked highly include:

  • Machine learning algorithms
  • Data mining and data analytics

More Information

Please contact Dr Shuxiang Xu for more information.

Closing Date

31st October 2020*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Sentiment analysis (also known as opinion mining) refers to the use of natural language processing and text analysis to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state (the emotional state of the author when writing), or the intended emotional communication (the emotional effect the author wishes to have on the reader).

The rise of social media such as blogs and social networks has fuelled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. Companies look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content, and actioning it appropriately. This project aims at employing Machine Learning algorithms to automatically detect sentiment in user reviews of interested online business websites.

Eligibility

The following eligibility criteria apply to this project:

  • The project is open to Australian (domestic) and international candidates
  • The PhD must be undertaken on a full-time basis
  • Applicants must already have been awarded a first class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills
  • Applicants must meet English requirements, or be able to do so before commencement
  • Candidates must demonstrate experience and strong interest in Machine Learning or general computational intelligence

More Information

Please contact Dr Shuxiang Xu for more information.

Closing Date

31st December 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, fuzzy sets, neural networks and evolutional systems have been proposed as a useful and effective framework for the modelling and understanding of brain activity patterns as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). However, most of the research so far has focused on lab-based applications in constrained scenarios, which cannot be extrapolated to realistic field contexts. Considering the decoding of brain activity, the computational Intelligence models, including fuzzy sets, neural networks, and evolutional computation, provide an excellent tool to overcome the challenge of learning from brain activity patterns that are very likely to be affected by non-stationary behaviours and high uncertainty. The application of computational Intelligence methods to learning and modeling​ has recently demonstrated its remarkable usefulness for coping with the effects of extremely noisy environments, as well as the variability and dynamicity of brain signals. Additionally, neurobiological studies have suggested that the behaviour of neural cells exhibits functional patterns that resemble the properties of intelligent computation to encode logical perception. This paves the way for developing new computational intelligence techniques based on intelligence abstractions that foster the capabilities for modeling and understanding brain function from a quantitative point of view.

Eligibility

Please refer to the Entry Requirements for a {Doctor of Philosophy/Master of Research} degree.

The following eligibility criteria also apply:

  • The project is open to domestic and international candidates
  • The PhD must be undertaken on a full-time basis
  • Applicants must already have been awarded a first class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector

Selection Criteria

Knowledge and skills that will be ranked highly include:

  • Applicants must be able to demonstrate strong research and analytical skills
  • Data Mining and Predictive Analytics Skills
  • Foundational programming skills
  • Statistics

More Information

Please contact Zehong Cao for more information.

Closing Date

31st December 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

In this project, HDR students implement and develop of state-of-the-art machine learning and deep learning models, especially in deep reinforcement learning algorithms to easily train intelligent agents for various games. The research goal is to speed up the learning process of multiple agents and allow each agent receives higher rewards in a game scenario. These trained agents can be presented in the demo workshop and can be used for multiple purposes, including testing of game builds and controlling behaviour.

In this project, we used the OpenAI Gym and Unity platform, which have been developed for creating and interacting with simulation environments. Specifically, the Unity ML Agents Toolkit is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. This project will use this toolkit to develop dynamic multi-agent interaction, and agents can be trained using reinforcement learning, imitation learning, neuro-evolution, or other machine learning methods through a simple-to-use Python API.

Additionally, this project is mutually beneficial for both students and AI researchers as it provides a central platform where advances in AI can be evaluated on rich environments and then made accessible to the industry and research developer communities.

The following eligibility criteria apply to this project:
  • See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree
  • The project is open to domestic and international candidates
  • Research must be undertaken on a full-time basis
  • Applicants must already have been awarded a first class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills
  • Candidate from a variety of disciplinary backgrounds are eligible to apply
Selection Criteria
  • Data Mining and Predictive Analytics Skills
  • Strong programming skills
  • Statistics experience
Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Zehong Cao for further information.

Closing Date

31st December 2020*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

The rapid growth of the Internet and web applications provide an overwhelming amount of information. Benefited from online social mediums and crowd computing platforms, it is very easy to collection information from various sources nowadays. In the process of information digitisation, the information of an entity can be generated from multiple sources and the information digitised or collected might be conflicted, with different qualities and even from fake or malicious sources.

It is crucial to find out the truth (truths) of an entity from different sources which provide information about the entity. However, for many web applications are operated under uncertain and dynamic environments. There may exist no evaluation standard for information quality or ground truth, and the information sources can be dynamic.

Under such environments, the discovery and mining of truth/truths is critical. In this project, we will investigate the use of advance AI and data mining techniques in estimation trustworthiness or shared or crowd sourced information.

Eligibility

Applicants from the following disciplines are eligible to apply:

  • Computer science
  • Mathematical sciences
  • Data analytics

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Assessment Criteria
  • The scholarship is open to Australian and New Zealand (domestic) candidates and to International candidates
  • Research must be undertaken on a full-time basis
  • Applicants must already have been awarded a first-class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills
Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Quan Bai for further information.

Closing Date

28th February 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Deep Learning recently lends itself extremely well to the research in computer vision domain where hierarchical structures of computational neurons can learn predictive features to effectively make predictive decisions. For example, in health care, deep learning is becoming also popular among medical imaging researchers who are looking for great tools to process a large number of images produced by scanners.

The impact of this to the society is potential and attract more and more attention from health care experts who have been looking for better methods to reduce the error rates in diagnosis. However, the most common deep learning models used for image processing are CNN-based which is a complex black-box consisting of millions of parameters that confused the experts of why the decisions are made. As a result, there is an increasing scepticism from those who do not want to use deep learning because of the lack of explainability.

In this research, the student will improve the transparency of deep neural networks to provide insights of the decision-making process. The topics of interest are (but not limited to):

  • Medical imaging (eye disease detection, knee pain prevention, etc.)
  • Visual reasoning, image captioning
Eligibility
  • The project is open to Australian (domestic) and international candidates
  • The PhD must be undertaken on a full-time basis
  • Honours degree/Master degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must meet English requirements, or be able to do so before commencement

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Son Tran for further information.

Closing Date

30th December 2020*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

As learner interaction in an online educational environment leaves a lot of digital traces behind, vast data sets of students’ online activities are available, which is known as Big data. Data and analytics in education, teaching and learning has attained great interest, resulting high-quality research into models, methods, technologies, and impact of analytics in education area. Big data and learning analytics with Artificial Intelligence (AI) is greatly extending the power of computers to revolutionise education sector. Educational data mining techniques discover meaningful patterns in these large datasets to create probabilistic and predictive models such as student success algorithms, understand and optimise learning and the environment. Learning analytics and AI are not panaceas for addressing all the issues and decisions faced by higher education but become part of the solution to enhance and transforms the way to support learning process.

The aim of the project is to investigate the deployment of AI techniques and analytical model/algorithm for improving learning analytics and for discovering the meaningful patterns in the large datasets of students to improve educational processes.

Eligibility
  • Experience with programming
  • Critical thinking

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Soonja Yeom for further information.

Closing Date

31st December 2020*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

The innovation of artificial intelligence and cognitive science aims to explore and simulate the complex and powerful information processing mechanism of the human brain and promote machines to a higher intelligence level as human brains. Visual neural calculation aims to "do what the brain does", the central idea of which is to explore the mysteries of the human visual system. It is a complex interdisciplinary problem to establish an appropriate neural computing model and simulate the visual information processing mechanism in the human brain so as to better extract feature information.

With the continuous development of brain cognitive science, there have been more opportunities for visual neural calculation. Its development direction is to investigate the knowledge learnt in the visual domain by popular pre-trained vision models (CNN-based framework) and use it to teach a recurrent model being trained on brain (EEG) signals to learn a discriminative manifold of the human brain's cognition of different visual object categories in response to perceived visual cues.

Eligibility
  • Data Mining and Predictive Analytics Skills
  • Strong programming skills
  • Statistics experience

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Zehong Cao for further information.

Closing Date

2nd April 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

In the last decade, there is an increasing trend towards adaptation using "Industry 4.0" in the organizations by connecting digital technologies, automation and big data with industry processes, products and logistics. Yet, its application in wood supply chains has not been fully investigated. In the forest sector, limited information is available that could offer opportunities for value adding to traditional forest products (e.g.logs, veneers) from each stand. This is because the limited data collected is primarily done manually adn there remains limited data mining. Additionally, information sharing within and along wood supply chains (pre- and post-harvest) remains limited. Enhancing  digital data collection and analysis prior, during and post-harvest operations will enhance efficient information supply and open up new options for industry 4.0 innovation in ways to significantly change supply chain processes.

This project investigates the application of industry 4.0 in wood supply chains through implementation of an " internet of trees and services (IoTS)" and aims to explore technical and socio-economic challenges and opportunities

Eligibility
  • The project is open to Australian (domestic) and international candidates
  • The PhD must be undertaken on a full-time basis
  • Applicants must already have been awarded a first-class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills
  • Applicants must meet English requirements, or be able to do so before commencement
  • Applicants must demonstrate experience and strong interest in Data Science, IoT and Blockchain

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Paul Turner for further information.

Closing Date

2nd April 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Agribusinesses including forestry remain an industrial pillar for Australia with 15%+ by value of Australia's total exports. However, it can be argued that sustainable production and consumption remains limited and that potentially valuable opportunities are being squandered while international competitors respond to customer demand for greater sustainability, traceability and carbon neutrality from business practices. Advanced techniques in generation and utilisation of digitalisation of the information within the supply chain provides opportunities to mitigate the adverse effects of the unsustainable consumption and production patterns. Industry 4.0 offers solutions to optimise the logistics networks and enhance both economic and environmental parameters in a circular economy are facilitating waste reduction, recycling and re-use. These solutions include Industrial Internet of Things (IoT), cloud computing (CC), Big data, Machine Learning, Human-Computer Interaction, Simulation, Augmented and Virtual Reality (AR/VR) and Cyber-security.

This project investigates the consequences of linking the industry 4.0 and Circular Economy in agribusiness for environmental and socio-economic benefit.

Eligibility
  • The project is open to domestic (Australian and New Zealand) and international candidates
  • The PhD will preferably be undertaken on a full-time basis
  • Applicants must already have been awarded a first-class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills
  • Applicants must meet English requirements, or be able to do so before commencement

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Paul Turner for further information.

Closing Date

2nd April 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Studies suggest the potential impact of blockchain in supply chains will be very significant and will contribute to improving the relationships between stakeholders through establishment of a safe, transparent and reliable "frictionless" method to exchange information and financial transactions. However, it is unclear to what extent these types of platforms are being used  in sustainable agribusiness, or to what extent they impose different challenges and risks especially on small and medium sized businesses who have limited supply chain power. Clearly facilitating any new technology to support sustainable production and consumption patterns and to reduce cost, risk and waste while increasing the product quality and market flexibility is positive – but is this happening and if not, why not?

This project investigates the potential designs and implementations of blockchains and investigates to what extent they are being deployed and to what effect. How is this technology impacting on track all used materials, including the dimensions of quality, quantity and ownership, over the whole supply chain in real-time?

Eligibility
  • The project is open to domestic (Australian and New Zealand) and international candidates
  • The PhD will preferably be undertaken on a full-time basis
  • Applicants must already have been awarded a first-class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills
  • Applicants must meet English requirements, or be able to do so before commencement

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Paul Turner for further information.

Closing Date

2nd April 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

In Australia, 320,000 tonnes of plastics waste were recycled in 2018 of which 46% were reprocessed in Australia and the rest exported. Options for continuing to export plastic waste are narrowing and consumers are increasingly demanding better management of plastic production, use, recycling and re-use to protect the environment and reduce pollution and carbon emissions. Reengineering supply chains is an integral part of improving plastic recycling in a circular economy but requires enhanced use of digital systems and use of geo-spatial data capture and data-mining tools and techniques. To-date limited studies provide evidence on the optimal way to re-design plastics supply chains or how best to mine aggregated geo-spatial data to reduce emissions and optimise re-cycling and re-use. Post-consumer plastic waste can be recycled up to six times, while heavily contaminated plastic waste impose high cost processing on existing logistics networks including special washing and drying.

This project will investigate how digitisation and use of GIS-based green supply chain management can be enhanced to address the existing challenges in Australia's plastics recycling supply chains. The aim will be to directly contribute practical models, tools and techniques to contribute to achieving a circular economy for plastics waste.

Eligibility
  • The project is open to domestic (Australian and New Zealand) and international candidates
  • The PhD will preferably be undertaken on a full-time basis
  • Applicants must already have been awarded a first-class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must be able to demonstrate strong research and analytical skills
  • Applicants must meet English requirements, or be able to do so before commencement

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Paul Turner for further information.

Closing Date

29th January 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Haptic technology is also widely used in education to enhance student's learning experiences with anatomy as it allows physical interaction with anatomical structures  (Kup-Sze, Hanqiu & Pheng-Ann 2003; Reid, Shapiro & Louw 2018; Yeom et al. 2013). It is evident that AR and haptic technologies encourage student learning of anatomy through exposure of the body visually by 3-D modelling, and physically with tactile feedback. There is a huge educational potential to apply AR and haptics in education of anatomy. However, it has not yet been widely researched or evaluated.

The purpose of the proposed research is to investigate the use of interactive 3D anatomical simulation, used in conjunction with haptic feedback, to determine if it improves students' learning. The research will compare the effectiveness of the combination of AR and Haptic technology to their use independently, as well as comparing it to existing learning methods, such as 2D images and interactive resources (CD/DVD).

The research will be undertaken into four stages:

  • Generation of interactive 3D anatomical models in a mobile device;
  • Applying haptic feedback when a user touches/interacts with the 3D models;
  • Integrating the simulation of AR with haptic feedback);
  • Comparison and Evaluation of effectiveness of AR/haptic education in anatomy against existing methods.
Eligibility

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Soonja Yeom for further information.

Closing Date

31st May 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Due to the advent of technologies such as 5G and IoT, the increase in network traffic has been exponential; consequently, presenting a larger set of opportunities for intrusion attacks on network traffic. Furthermore, the complexity and nature of these attacks can surely go undetected as they can easily be impersonated as normal behaviour (For example, DoS - Denial of Service attacks).

Due to high levels of work intensity and frequent turnovers, it is impractical for an organisation to leverage human intervention; especially, early-career engineers as the nature of this work requires higher understandings of hacking techniques.
In this project, we will conduct research that utilises Machine Learning to develop a model that can be applied to practice using payload detection in real-world Intrusion Detection (IDS) and Intrusion Prevention Systems (IPS). The targeted high detection rate of our model will significantly reduce the network payloads that need to be verified; consequently, overcoming human dependency.

Furthermore, we will devise a distributed methodology via Blockchain to detect not only network attacks but also intrusions based on abnormal behaviours that can be easily missed by an engineer. This methodology can be a novelty for Collaborative Intrusion Detection Systems (CIDS) to detect attacks such as Denial of Service (DoS) with high accuracy.

Eligibility
  • Strong research and analytical skills
  • Research and/or Development background in the areas of Blockchain, software architecture and distributed systems
  • Understanding of distributed application Development pertaining to Blockchain
  • Publication record or relevant industry experience

Applicants from the following disciplines are eligible to apply:

  • Computer Science
  • Software Engineering
  • Information Technology
  • Computer Engineering

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Muhammad Bilal Amin for further information.

Closing Date

31st May 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Blockchain is a technology to store data securely and transparently using distributed and crypto techniques. It is a data storage of the future with security, immutability, and transparency built-in. With such an evolutionary feature set, blockchain has currently gained traction in its adoption ratio. However, the required momentum to take this technology to the future is still lagging due to the core limitation of its inability to interoperate between heterogeneous and multiple chains.

Considering this an opportunity, in this project, we will research to find the resolution to the blockchain interoperability challenge that enables arbitrary data sharing among heterogeneous and multiple blockchain networks.

The current block in the chain is highly specialized and designed to handle transaction-oriented records; consequently, limiting the possibility of flexibility and extensibility required to achieve an interoperable distributed data structure. Therefore, for general-purpose storage and sharing of arbitrary data, the block structure needs to be generalized with schema-based definitions to allow global data interpretation. Therefore, in this project, we will research on devising a markup-like meta-structure definition scheme to represent a generic data block structure within a blockchain network. Furthermore, a possible markup-translation algorithm that can facilitate arbitrary data exchange among multiple and heterogeneous blockchains.

Eligibility
  • Strong research and analytical skills
  • Research and/or Development background in the areas of Blockchain, software architecture and distributed systems
  • Understanding of distributed application Development pertaining to Blockchain
  • Publication record or relevant industry experience

Applicants from the following disciplines are eligible to apply:

  • Computer Science
  • Software Engineering
  • Information Technology
  • Computer Engineering

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Muhammad Bilal Amin for further information.

Closing Date

31st May 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

In the past decade, blockchain has been one of the most revolutionary technologies that will have a far-reaching transformational effect across almost every industry in the coming years. A lot of organizations are examining its benefits for the sake of industries such as healthcare, law enforcement, asset management, forestry, agriculture, voting, and notarization. Given that every organization needs to share data, knowledge and assets; blockchains necessarily need to interoperate with each other.

Blockchain interoperability not only means the possibility to share and exchange, digital assets and arbitrary data but also to reference chain code across heterogeneous and multiple blockchain networks. However, the smart contract/chain code in a blockchain can be written in several different languages, thus, limiting the possibility of code reusability among blockchain networks.

In this project, we will conduct research to devise a chain code virtualization methodology for an interoperable blockchain ecosystem where the scale of execution of smart contracts is beyond a single block or a single chain deployment. Thus, enabling a new generation of distributed applications that can be built on the aggregation of smart contracts, written in different languages, like workflows and orchestrations

Eligibility
  • Strong research and analytical skills
  • Research and/or Development background in the areas of Blockchain, software architecture and distributed systems
  • Understanding of distributed application Development pertaining to Blockchain
  • Publication record or relevant industry experience

Applicants from the following disciplines are eligible to apply:

  • Computer Science
  • Software Engineering
  • Information Technology
  • Computer Engineering

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Muhammad Bilal Amin for further information.

Closing Date

1st October 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filler earlier

The Research Project

Trust is a term used in many fields, including computer science, and has many different meanings [1] [2]. In this project, trust is used to generate some expectation of success in a collaboration between two separate entities/agents. Most of trust models assume single and homogeneous trust relationship between agents [3}. However, most of these models cannot handle dynamic environments.

Contextual information plays important roles in trust evaluation. Especially as ground truth is not available in many complex environments, trust is closely related with contextual factors including social relationships among entities, spatial temporal information, features and types of services, etc. To overcome some limitations in existing trust mining approaches, in the research we will investigate how to utilize contextual information in trust mining and develop a robust mechanism which can allow more accurate and reasonable trust evaluations.

In this project, the student will propose a context-aware trust model, which can take contextual information into trust analysis. The proposed model will be applied in open dynamic environments, and to improve collaborations among agents with different capabilities and skills, i.e., heterogeneous. Simulation-based experiments will be conducted to evaluate the performance of the proposed model.

References:

  1. Marsh, S.P., Formalising trust as a computational concept. Ph.D. dissertation, University of Stirling, Apr. 1994.
  2. Sabater, J. and C. Sierra, REGRET: reputation in gregarious societies, in Proceedings of the fifth international conference on Autonomous agents. 2001, ACM: Montreal, Quebec, Canada. p. 194-195.
  3. Tang, J., H. Gao, and H. Liu, mTrust: discerning multi-faceted trust in a connected world, in Proceedings of the fifth ACM international conference on Web search and data mining. 2012, ACM: Seattle, Washington, USA. p. 93-102.
Eligibility
  • The project is open to Australian (domestic) and international candidates
  • The PhD must be undertaken on a full-time basis
  • Honours degree/Master degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must meet English requirements, or be able to do so before commencement

Applicants from the following disciplines are eligible to apply:

  • Computer Science
  • Information and Computing Technologies

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Dr Quan Bai for further information.

Closing Date

1st December 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filler earlier

The Research Project

Knowledge graphs (KGs) are large networks of entities and their semantic relationships.  It has been widely applied in multiple areas, including information retrieval, situation awareness and recommender systems. A KG can be represented as a set of triples (h, t ,r) in which h (head) and t (tail) are entities (nodes), and r (relation) is  the relation (edge) between the two entities. KG embedding is to represent the entities and relations in a continuous vector space. This is a critical process to make KG semantic meaningful and machine understandable, and normally achieved by using machine learning methods. Negative sample generation is an important process for KG embedding. It provides sufficient training samples for the KG embedding, and fill the vector to a continuous space.

KG was first designed to formalize unstructured natural language data. With the development of KG techniques, researchers are now exploring the use of KG in other domains, especially IoT, Cyber Physical Systems (CPS) and Cybersecurity. However, traditional KG mining and KG embedding methods have been mainly focused on NLP data, and are not suitable for the latest applications. This project will investigate the limitations of existing KG embedding and mining methods, and design novel algorithms that can mine KG data more effectively and handle the dynamics from complex application domains.

  1. Yongqi Zhang, Quanming Yao, Yingxia Shao and Lei Chen, NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding, https://arxiv.org/pdf/1812.06410.pdf
  2. Yantao  Jia, Yuanzhuo  Wang, Xiaolong  Jin, Hailun  Lin, Xueqi  Cheng , Knowledge Graph Embedding: A Locally and Temporally Adaptive Translation-Based Approach, ACM Transactions on the Web (TWEB), 2017
Eligibility
  • The project is open to Australian (domestic) and international candidates
  • The PhD must be undertaken on a full-time basis
  • Honours degree/Master degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
  • Applicants must meet English requirements, or be able to do so before commencement

Applicants from the following disciplines are eligible to apply:

  • Computer science
  • ICT
  • Mathematical sciences
  • Electrical engineering

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Dr Quan Bai for further information.

Closing Date

16th November 2020

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

The Research Project

Knowledge of the hydraulic properties of a soil is important for modelling soil moisture and for informing simulations to assist decision making in agriculture.  Determination of these parameters typically requires extensive and expensive field or laboratory investigations.

The purpose of this research is to explore the potential to determine hydraulic parameters using data from in situ soil moisture probes. The approach being investigated uses in situ moisture readings to provide feedback to a soil moisture model being run in parallel, in a technique known as Data Assimilation.  This will require application of data cleaning procedures, evaluation and execution of a range of data assimilation procedures including Ensemble Kalman & Particle filter, use of pedotransfer functions, development of field based data training routines, as well as some laboratory analysis of soil properties.

In addition to data assimilation, this research will attempt to leverage a range of machine learning and time series analysis techniques to address algorithm limitations when applied to real-world soils, especially those associated with wetting and drying hysteresis or complex soil profiles.

Eligibility
  • Research must be undertaken on a full-time basis
  • Applicants must already have been awarded a first class Honours degree or suitable industry experience, or Masters of research degree with a minimum research component
  • Applicants must be able to demonstrate strong research and analytical skills
  • Candidates from a variety of disciplinary backgrounds including ICT, mathematics, engineering and physics are encouraged to apply

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Dr Marcus Hardie for further information.

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