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Bayesian statistics offer a powerful paradigm for the development of scientific knowledge. Our research includes probabilistic modelling, stochastic computation, the use and implementation of Monte Carlo Markov Chains, and other approaches in Bayesian inference and decision theory.
We work on the development of computationally efficient statistical methods for complex models with applications in genetics, and MCMC algorithms with applications in epidemiology, geophysics and transportation.
The success of businesses, government and institutions is increasingly dependent on their ability to transform data into information, insights and novel data-products. We have expertise in data analysis in a number of industry sectors to work with large datasets, both stored and incoming to discover knowledge in the mountains of data.
We apply cutting-edge methods from computer science and statistics to model patterns and relationships, typically using large datasets. We research and apply techniques such as machine learning, image analysis, and pattern recognition to model data from a wide variety of fields including business analytics, social media analysis, genetics and ecology.
The plethora of data available in large systems (such as genetics, ecology, bioinformatics and the health sciences) requires the analysis of multiple variables simultaneously. We research and develop rigorous statistical methods for the analysis and visualisation of high-dimensional systems, graphical models, ordination, and spatial statistics.
We create statistical tools for ecological applications, including models of ecological systems, species’ abundances, biodiversity and community ecology. We engage in active field-based ecological research, and consulting for environmental monitoring and impact assessment.
Our scientists are developing new methods to assess quality in manufacturing processes. We have particular expertise in applications in the dairy industry.
We have expertise in statistical modelling and inference for transportation systems. Particular emphases include the development and analysis of models to describe the day-to-day dynamics of traffic flows on road networks, and methods of inference for traffic models using low-dimensional data (network tomography).
We work on statistical problems in geophysics. Our focus is on the spatio-temporal estimation of hazard, especially from volcanoes or earthquakes, and the assessment of the risk.
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The key focus will be on achieving positive outcomes for landscape and community resilience to meet Māori aspirations locally, regionally and nationally. There is flexibility to adjust the project scope and methodology according to the successful candidate’s interests, experience and expertise. The successful candidate will work alongside researchers and scientists in modelling, biophysical science, economics, social sciences and other disciplines to improve the way we provide information through risk-hazard modelling as a basis for sustainable planning of natural hazards, risk management, and improving resilience.
The purpose of this PhD scholarship is to encourage and support doctoral research investigating dynamic assessment of multi-capital and wellbeing value chains under coincident and cascading natural hazards events. This includes assessment of value chains through time, across space, for different socio-economic agents including households, businesses, government and not-for-profit organisations.
The project is concerned with modelling the effect of multiple impacts to infrastructure by the same or different natural hazards. Impact from a previously occurring hazard can make the infrastructure more vulnerable to subsequent hazards. The requirement is to develop a vulnerability model (incorporating type of infrastructure, multiple hazard types, the magnitude of the subsequent hazards, the damage state from the earlier hazards, and repairs).
Marti Anderson received $936,000 for her research project "Not too hot, not too cold, just right: New models of species’ responses to their environment". This project aims to quantify and predict global-scale responses of ecological communities to environmental change.Distinguished Professor Marti Anderson
Marsden funding for new models of species’ responses to their environment.