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Modelling and managing uncertain Antarctic species networks

Antarctic ecosystems are complex, and data is limited since it is expensive to collect. Species including penguins, seabirds, invertebrates, mosses, and marine species interact in food webs which can be modelled as mathematical networks. These networks can be large, span across terrestrial and marine systems, and are changing in response to environmental changes.These ecological networks can be modelled using differential equation predator prey models like Lotka-Volterra to describe these interactions. However, the relationships between species are not always known, or …

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

Parameter identifiability for stochastic processes in biological systems

Stochastic models are used in biology to account for inherent randomness in many cellular processes, for example gene regulatory networks. Noise is often thought to obscure information, however, there is an increasing understanding that some randomness contains vitally important information about underlying biological processes.When applying these models to interpret and learn from data, unknown parameters in the model need to be estimated. However, not all data will contribute to a given estimation task regardless of the data quantity and quality. …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Making the most of many models

In the age of Big Data, machine learning methods, and modern statistics the adage "all models are wrong but some are useful" has never been so true. This project will investigate data science approaches where more than one model makes sense for the data. Is it better to choose a single model or is there something to be gained from multiple models?This project will look at variable selection methods, penalised regression, Bayesian model averaging and conformal prediction. The research has …

Study level
Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Uncertainty quantification in mathematical ecology

In this project, we will explore uncertainty quantification in mathematical ecology via a number of different methods. The project will involve application to ecological dynamics in the Great Barrier Reef.

Study level
Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences

Small business resilience in times of economic uncertainty: Examining retailers and regional businesses

Regional Australia is undergoing significant structural, economic, social, and environmental change which is impacting the viability of small businesses (Regional Australia Institute, 2018). Regional small retail businesses, estimated to contribute $21.9bn to local economies (Australian Small Business and Family Enterprise Ombudsman, 2019) are particularly susceptible to economic shocks, have lower survival rates, more volatile revenues and are generally less resilient than larger business (Barraket, Eversole, Luke & Barth, 2019).Disruptive external events such as the acceleration of e-retailing, COVID-19 travel restrictions, …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Business and Law
School
School of Advertising, Marketing and Public Relations
Research centre(s)

Australian Centre for Entrepreneurship Research

Resolving uncertainty in decisions to improve agri-food system outcomes for people and nature

Despite efforts to monitor and manage declining species and ecosystems around the world, biodiversity is still not routinely included in mainstream decision-making and continues to decline at the highest rate in human history. Added to this is the problem that both natural and agri-food systems are complex networks that are continually changing due to human and natural disturbances, with climate change likely to increase the impacts of extreme events like drought, fire and economic shocks on these networks.Because of large …

Study level
PhD
Faculty
Faculty of Science
School
School of Biology and Environmental Science
Research centre(s)
Centre for Data Science
Centre for the Environment

Deep learning for robotics in open-world conditions: Uncertainty, continuous learning, active learning

In order to fully integrate deep learning into robotics, it is important that deep learning systems can reliably estimate the uncertainty in their predictions. This would allow robots to treat a deep neural network like any other sensor, and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements or to accumulate information over time.Deep learning systems, e.g. for classification or detection, typically return scores from their softmax layers that are proportional to …

Study level
PhD
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

Optimal conservation management in uncertain Antarctic environments

Species and ecosystems in Antarctica are threatened. Optimal biodiversity conservation is an interdisciplinary field combining mathematical modelling and optimisation with ecology and conservation. We can use mathematics to understand the system, model how management actions might impact it, and then optimise which actions should be used. For example, we can explore where protected areas should be placed, how species should be managed, or how tourist impacts should be reduced. However, the complexities of conservation in Antarctica necessitate the application of …

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

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, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

Curtailing corporate tax aggression through uncertain tax benefits

Over the last five years Australia has adopted numerous measures to address aggressive corporate tax practices. A recent addition to these measures (on the 1st of January 2019) is AASB Interpretation 23 - Uncertainty over Income Tax Treatments, which was developed to clarify the treatment of uncertain tax positions. This interpretation requires entities who produce general purpose financial reports (GPFRs) to disclose uncertain tax benefits (UTB) in the notes to their financial statements. Extant literature suggests that UTB disclosures are …

Study level
Honours
Faculty
Faculty of Business and Law
School
School of Accountancy

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