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Found 477 matching student topics

Displaying 13–24 of 477 results

Surprising genomes

Genomic sequencing has changed radically since the first public sequencing projects more than 25 years ago. The original human genome project cost more than two billion dollars; sequencing a human genome now costs as little as a thousand, and we may sequence whole viruses and bacteria as a matter of routine.The challenge now lies in rapidly analysing these genomes as they appear, and understanding quickly whether there is anything interesting in the new sequence to warrant further inquiry. This project …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Data-driven and process-aware workforce analytics

Modern information systems in today’s organisations record massive amount of event log data capturing the execution of day-to-day core processes within and across organisations. Mining these event log data to drive process analytics and knowledge discovery is known as process mining. To date various process mining techniques have been developed to help extract insights about the actual processes with the ultimate goal to organisations' workforce capability and capacity building.As an important sub-field of process mining, organisational mining focuses on discovering …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Information Systems
Research centre(s)
Centre for Data Science

Advanced materials for redox flow batteries

Grid-scale energy storage for intermittent renewables like solar and wind is an essential element of the transition away from fossil fuel based electricity production. Redox flow batteries have some very interesting characteristics for this stationary storage application:they are safer than other battery typesthe amount of energy stored can typically be scaled up easilythe power and energy of a system are more decoupled compared to lithium and other batteries, making them flexible in their design parameters.Ion exchange membrane and electrode are …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Chemistry and Physics
Research centre(s)
Centre for Materials Science

Praeclarus process-data quality framework

Praeclarus is an open-source software framework that aims to facilitate data pre-processing for process mining. Process mining is specialised data mining focusing on process-data. It is of high interest to industry, with the market doubling every two years (e.g., increasing from $550M in 2020 to $1B in 2022). This market increase has meant that big companies like Microsoft, SAP, and IBM are acquiring process mining vendors such is Minit, Signavio, and myInvenio.Recent process mining surveys show that more than 60% …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Information Systems

Habitable water infrastructures

This project explores buildings, public/civic spaces, and landscapes as water infrastructure. Water is integral to human survival; hence understanding buildings and urban spaces as habitable water infrastructure has the potential to mitigate the effects of the climate crisis and navigate too much water (floods) and too little water (drought) while offering different modes of occupation.With increasing rainfall intensities, floods, rising sea levels, and drought, the pervasive dichotomy between habitable spaces and water infrastructures can no longer hold. The two can't …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Engineering
School
School of Architecture and Built Environment

Representation learning for anti-microbial resistance

This project is about using neural network models help us understand Anti-Microbial Resistance (AMR), a phenomenon in which bacteria adapt to reduce the effectiveness of antibiotics, usually through a process known as Lateral or Horizontal Gene Transfer - where genes are included in the organism from other sources.Our focus will be on learning compact vector representations of biological sequences known to be associated with AMR genes. By encoding DNA sequences in this way we can more rapidly identify AMR genes …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Visualisation and sonification for genomic data sets

Successive revolutions in sequencing technology over the past two decades have led to an explosion in the availability of genomic data. Analysing biological datasets and identifying relationships within them is challenging - some of the process can be automated but interactive exploration offers a number of advantages, and supports serendipitous discovery.This project looks at visual analytics and sonification - the use of sound and musical encodings - to enhance our understanding of biological networks.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

Process-data governance patterns

Data is recognised a strategic asset for organisations. There is a growing need to manage the voluminous data an organisation is exposed to in order to use it for decision-making.Of particular significance is process data, which consists of information about the execution of processes. Such information is used to uncover behaviour of processes within an organisation. This brings forth the significance of data governance. Data governance is the exercise of control and authority over management of data. Despite its significance, …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Information Systems

Unified def-site and use-site security policies for component-based software systems

Securing the information manipulated by computer systems, such as privacy and integrity in social software, is a challenge. Traditional methods to impose limits on the information disclosure, such as access control lists, firewalls, and cryptography, provide no guarantees about information propagation. For instance, cryptography provides no guarantees about the confidentiality of the data are given once it is decrypted.Information flow control (IFC) is the problem of ensuring secure information flow according to specified policies within computer systems. Modern applications are …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Science
School
School of Computer Science

Productive reproducible workflows for deep learning-enabled large-scale industry systems

Deep learning is a mainstream to increase the capability of industry systems, particularly for those with massive data input and output. It is seen that many tools are now claimed to be freely available and could facilitate such process of development and deployment significantly with scalability and quality.However, limited attention has been on developing reproducible and productive workflows to identify the tools and their values towards large-scale industry systems. In this project, we will explore how to design such a …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Science
School
School of Computer Science

Systematic evaluation towards the analysis of open-source supply chain on ML4SE tasks

Applying machine learning algorithms to source code related SE task is rapidly developing and attracts the attention from both researchers and industry engineers. While there are many program languages available, applying such techniques, i.e., the representation learning models, for different languages may achieve different performance. Particularly, they all have their own strict syntax, which determines the abstract syntax tree. Thus, a lot of different open-source supply chain are available, for example the parsing tools are used to build AST from …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Science
School
School of Computer Science

Fine-grained software vulnerability detection using deep learning techniques

Software vulnerability is a major threat to the security of software systems. Thus, the successful prediction of security vulnerability is one of the most effective attack mitigation solutions. Existing approaches for software vulnerability detection (SVD) can be classified into static and dynamic methods. Powered by AI capabilities, especially with the advancement of machine learning techniques, current software has been produced with more sophisticated methodologies and components. This has made the automatic vulnerability proneness prediction even more challenging. Recent research efforts …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Science
School
School of Computer Science

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