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Found 241 matching student topics

Displaying 193–204 of 241 results

Mathematical modelling of ecosystem feedbacks and value-of-information theory

Ecosystems respond to gradual change in unexpected ways. Feedback processes between different parts of an environment can perpetuate ecosystem collapse, leading to potentially irreversible biodiversity loss. However, it is unclear if greater knowledge of feedbacks will ultimately change environmental decisions.The project aims to identify when feedbacks matter for environmental decisions, by generating new methods that predict the economic benefit of knowing more about feedbacks. Combining ecological modelling and value-of-information theory, the outcomes of these novel methods will provide significant and …

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

Addressing security challenges for the industrial internet of things

With the emergence of the Internet of Things (IoT) and Industry 4.0, there is a trend for applying these services and applications to a large-scale industrial area. The IoT paradigm has changed the way of interactions with the things that surround us. In essence, the IoT promises ubiquitous connection to the Internet, turning common objects into connected devices. It is predicted that there will be 50 billion connected devices at the end of the year 2022.Over the last few years, …

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

Optimal ecosystem management in rapidly changing systems

Delays in acting in collapsing ecosystems can be catastrophic. With every passing year, the chances that the ecosystem has progressed past some point of no return increases. Yet the research and development needed to develop a new technology can take a long time. Balance between these two dynamic processes is needed to determine the optimal length and effort for developing new technologies. This project will develop a method for finding the optimal schedule for developing technological readiness, social acceptability, a …

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

Hierarchical visualisation of large social networks

Networks have been extensively used to capture social interactions, by representing individuals as nodes and their relationships as edges.Such networks have been used to model the spread of epidemics. A few nodes are 'infected', and over time they gradually infect their neighbours on the network, who in turn infect their neighbours, etc. This type of model can then be used to simulate different intervention strategies aimed at containing outbreaks.However, an important limitation is the difficulty to visualise these networks when …

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

Modelling and managing uncertain Antarctic species networks

Antarctic ecosystems are complex, and data is limited since it is expensive to collect. Species interact in food webs which can be modelled as mathematical networks. The relationships between species are not always known, or we might know they interact but not how strongly. Noisy (or imperfect) data can be used to model these species interactions to give more certainty about how the ecosystem works as a whole – although the worse the data is, the less information it contributes. …

Study level
Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Visualisation of CRISPR targets

CRISPR-Cas9 technology allows us to modify virtually any gene in any organism of interest. It has generated a lot of interest, both in the research community and the general population.One of the crucial components of CRISPR experiments is the design of the 'guide RNAs' that will control where modifications occur.We have developed a software pipeline, named Crackling, to identify safe and effective guide RNAs across entire genomes. We're now seeking to develop a visualisation to communicate the results produced by …

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

Conservation is a noisy business: modelling the effects of stochasticity on wildlife management decisions

To conserve species in disturbed natural environments, we need to use mathematical models to predict the consequences of different interventions. Unfortunately, these models are based on partial information of complex systems, and the systems themselves are subject to substantial observational and process noise.We often use ordinary differential equations to describe ecosystems, like the classic logistic growth model:dn/dt = r n (1 - n / k)However, these models are deterministic, and they assume we know the values of the key parameters …

Study level
Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

AI-Based Data Analysis on Multiple Imaging Modalities

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. According to the World Health Organization (WHO), it is estimated CVD takes 17.9 million lives every year. In Australian, the statistical data from the Australia Heart Foundation shows CVD is a major cause of death in Australia. It occupies 26% of all deaths, responsible for an average 118 deaths every day. Four of the main types of CVD are coronary heart disease, strokes and transient ischaemic attack, peripheral …

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

Data reasoning to extend domain knowledge in deep learning

A wide variety of companies now use personalized prediction models to improve customer satisfaction, for example, detecting cancer relapses, Detecting Attacks in Networks (e.g., SDN) or understanding Customer Online Shopping Behaviour. However, the dramatic increase in size and complexity of newly generated data from various sources is creating a number of challenges for domain experts to make personalized prediction.For example, early detection of cancer can drastically improve the chance and successful treatment. Recently, supervised deep learning has brought breakthroughs in …

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

Predicting alternative states induced by multiple interacting feedbacks: seagrass ecosystems as a case study

This project seeks to explore the complex dynamics that might arise from multiple interacting feedbacks in marine ecosystems, by designing ordinary and/or partial differential equation models of these feedbacks and analysing the steady states and/or temporal dynamics of the proposed model(s).It has been hypothesised that many social and ecological systems exhibit alternative stable states due to feedback processes that keep the ecosystem in one state or the other. The result can be tipping points, which are difficult to predict but …

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

Virtual leaves: from data to surfaces and the steps in-between

Like all industries, agriculture is benefiting from the data and computing revolution. Using hand-held scanners, CT scanners, or other technologies, we can acquire data sets that represent real leaves of agricultural crops, e.g. wheat. Using this data, and performing many intermediate steps, we can build virtual leaf surfaces that can be used in computer models to perform simulations of droplet impactions, spreading, evaporation, and other phenomena of interest to the industry.This project concerns the 'many intermediate steps', for which there …

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

Hospital readmission prediction with domain knowledge

The Australian Commission on Safety and Quality in Health Care (the Commission) has highlighted that reducing avoidable hospital readmissions supports better health outcomes, improves patient safety and leads to greater efficiency in the health system. Previous studies have reported that up to 11% of the Emergency (ED) population are "heavy users" with a higher prevalence of psychosocial problems and often co-existing chronic medical conditions. All Australian governments have committed to reforms under the National Health Reform Agreement Addendum,1 and the …

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

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