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Found 443 matching student topics

Displaying 49–60 of 443 results

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

Playing Tetris with Australian threatened species

Many of Australia's threatened species can only avoid extinction if we keep them on islands or behind fences, where foxes and cats can't kill them all. We call these places "safe havens".Some species can only exist in some safe havens. Maybe they need particular habitats, or particular temperatures, and these can't be found everywhere.Some pairs of species can't live together. Maybe one is a predator of another. Maybe they fight too much.So, we need to find a way to put …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)

Centre for the Environment

Optimisation of piezoelectric materials for robotics applications

Piezoelectricity, which translates to “pressure electricity”, is the phenomenon in which certain materials convert mechanical energy to electrical energy, and vice versa. Such materials are common-place and are used in a variety of applications including sensor, actuator, and energy harvesting technologies. The capabilities of such piezoelectric materials have not yet been fully realised. We plan to use computational structural optimisation to design new piezoelectric materials and components that may contribute to novel sensing technologies for robotics applications. Essentially, robots need …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences

Information retrieval and coding methods for large scale bioinformatics

Advances in sequencing technologies over the past two decades have led to an explosion in the availability of genomic sequence data and an increasingly urgent need for scalable clustering and search facilities. One approach is to encode sequences as binary vectors in a high-dimensional space, simplifying the comparison and allowing it to be computed very rapidly using bit-level operations.Coupled with these ideas is the need to provide clustering methods and efficient indexing and lookup in response to search queries. One …

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

Facilitating gaining trust in IOT systems

Many organisations have shown an increasing interest in deploying IOT systems. However, most of them and their stakeholders are new to these systems, and it is difficult for them to trust the technology. What are the technological, managerial and societal aspects that contribute to trust in IOT systems? What can we do to improve the level of trust and increase adoption of the technology?

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

Facilitating gaining trust in AI

Artificial intelligence (AI) technologies are automating service delivery in many sectors. Businesses have shown interest in using these technologies for delivering complex services in a way that meet the unique needs of customers. The technology gained more popularity particularly during Covid-19 outbreak, as it helped organisations to become more efficient in service delivery and increased service availability for customers / service applicants. However, gaining managers’ and users’ trust in these systems has always been a significant challenge. Particularly, managers and …

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

Mathematical modelling of cell-to-cell communication via extracellular vesicles (EVs)

Extracellular vesicles (EVs) are membrane bound packages of information constantly being released by all living cells, including bacteria. There are many types and sizes of EVs. Each EV type contains its own distinctive cargo consisting of characteristic DNA, RNA, and proteins. We are just beginning to understand the many roles of EVs to maintain the health of the cell producing the EVs, and to communicate with other cell types that take up the EVs produced by neighbouring cells. Since EVs …

Study level
Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences

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

Multi-modal sentiment analysis

In deep learning models, language models and word embedding methods have become popular to understand the context of text data. Popular language models such as BERT have limitations in terms of the token length. There exist some corpora that have longer text with an average of 1000 tokens. Additionally, these corpora are text-heavy and only include some images.In our prior works, we have developed several multi-modality models on social media datasets.

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

Evaluation of language models and word embedding methods for natural language processing applications

In deep learning models, language models and word embedding methods have become popular to understand the context of text data. There exist many variants of these methods and have different limitations. This project will introduce you to the hot topic of language models and the fields of Natural Language Processing and Text Mining. 

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

Curvature dependence of reaction-diffusion wave front speed with nonlinear diffusion.

Reaction-diffusion waves describe the progression in space of wildfires, species invasions, epidemic spread, and biological tissue growth. When diffusion is linear, these waves are known to advance at a rate that strongly depends on the curvature of the wave fronts. How nonlinear diffusion affects the curvature dependence of the progression rate of these wavefronts remains unknown.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Biomedical Technologies

Optimising bone shape with memory networks

Bone is a dynamic tissue that optimises its shape to the mechanical loads that it carries. Bone mass is accrued where loads are high, and reduced where loads are low. This adaptation of bone tissue to mechanical loads is well-known and observed in many instances. However, what serves as a reference mechanical state in this shape optimisation remains largely unknown.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Biomedical Technologies

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