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Found 13 matching student topics

Displaying 1–12 of 13 results

Developing predictive models, methods and analytics for complex sports data

A 3-year strategic partnership on sports data science between the Centre for Data Science (CDS), the Australian Institute of Sport (AIS) and the Queensland Academy of Sport (QAS) was launched in the past few months. With a drive towards data informed decision making across the high performance sports network nationally, a number of collaborative, interdisciplinary research and scholarship opportunities ranging from VRES, to honours, masters and PhD have developed.

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

Statistical methods for detecting Antarctic ecosystems from space

Satellite images are a frequent and free source of global data which can be used to effectively monitor the environment. We can see how the land is being used, how it’s being changed, what’s there – even where animals are in the landscape. Using these images is essential, particularly for regions where data is expensive to collect or difficult to physically access, like Antarctica. In Antarctica and the sub-Antarctic islands, satellite images can be an easy and quick way to …

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

Seasonal analysis in R

Many health conditions are seasonal and understanding the seasonal patterns can help better understand the causes of disease. This project will examine an existing and popular set of R programs used to statistically model seasonal patterns, and examine where the code could be improved. The outcome will be a new and improved version of the software, with the student as a co-author.

Study level
Vacation research experience scheme
Faculty
Faculty of Health
School
School of Public Health and Social Work
Research centre(s)
Centre for Data Science

Modeling Australian rainfall extremes using extreme value methods

To mitigate the risk posed by extreme rainfall events, we require statistical models that allow us to extrapolate outside the range of our observed data into the tail of our distribution and to estimate the probability of record events we are yet to observe.For this type of statistical analysis, we typically use methods from extreme value theory. The challenge for modelling rainfall extremes using many of these extreme value methods is that there exists a gap between theory and practice. …

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

Second Wind: Extending official wind records with citizen science observations

High-quality observations are the cornerstone of meteorology and climate science. Personal weather stations (PWSs) from 'citizen scientists' have the potential to fill some of the large observing gaps between official measurement stations and to contribute additional observations.The challenge is that observations from citizen science stations are not quality assured, and garbage in equals garbage statistical inference out. Work is needed to verify citizen science wind observations to make the data suitable for use in fields such as statistical post-processing, data …

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

Predicting player performance from one format to another in cricket

Identifying talent as early as possible in elite sport is critical. An important component of this is learning about what metrics of performance in lower grades to focus on to help predict performance in the top grade. This project will explore for this research problem for cricket.

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

Designing efficient sampling algorithms for ensemble ecosystem modelling

This project seeks to use a combination of matrix algebra and advanced Monte Carlo methods for rare-event simulation to identify efficient algorithms for ensemble ecosystem modelling in large ecosystems. This project has the potential to significantly change the methods that researchers useto investigate complex ecosystems for the purposes of environmental management and future predictions.Ensemble ecosystem modelling (EEM) is a novel and increasingly popular method for generating predictions of future species abundance in complex ecosystems represented by generalisations of the Lotka-Volterra …

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

Spatial statistics for microchemical maps of rocks

Synchrotron X-ray Fluorescence Microscopy (XFM) provides exceptionally detailed, large multi-element microchemical maps of thin rock specimens. Together with conventional optical and electron-beam imaging methods, XFM maps permit novel insights into coupled mass transfer processes at the grain scale controlling important Earth processes such as earthquake formation, volcanism, reactive fluid flow, and CO2-sequestration.However, to this date, most scientific work using XFM is mostly qualitative and does not make full use of the statistical information that can be obtained from the data. …

Study level
Vacation research experience scheme
Faculty
Faculty of Science
School
School of Earth and Atmospheric Sciences

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

Topics in computational Bayesian statistics

Bayesian statistics provide a framework for a statistical inference for quantifying the uncertainty of unknowns based on information pre and post data collection.This information is captured in the posterior distribution, which is a probability distribution over the space of unknowns given the observed data.The ability to make inferences based on the posterior essentially amounts to efficiently simulating from the posterior distribution, which can generally not be done perfectly in practice.This task of sampling may be challenging for various reasons:The posterior …

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

Computational drug repurposing for neuropsychiatric disease

While hundreds of robust genetic associations have been found for neuropsychiatric disease (such as schizophrenia, major depression, and anxiety) understanding the exact molecular mechanisms leading to disease onset and progression remains challenging. Inherited (i.e. genetic) risk factors for many neuropsychiatric diseases converge on genes that are co-ordinately expressed (co-expressed) in a disease-relevant tissue (e.g. brain). The study of how genetic risk factors affect co-expressed genes (i.e. gene co-expression analysis) has the potential to uncover new biological processes underlying disease onset. …

Study level
Honours
Faculty
Faculty of Health
School
School of Biomedical Sciences

Applications of bioinformatics and statistical modelling in genomics and personalised health

Prostate cancer is one of the leading causes of mortality and is the most diagnosed cancer among men in Australia. The identification of novel biomarkers in prostate cancer could be valuable in the design of therapeutics and the identification of their molecular targets. Previous prostate cancer bioinformatic studies have recognised risk-associated genetic components as potential biomarkers.This project will apply statistical and mathematical approaches to identify novel biomarkers in prostate cancer.

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Chemistry and Physics
Research centre(s)
Centre for Genomics and Personalised Health

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