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Mathematics for large-scale inference algorithms in Bayesian demographic analysis

Department of Mathematics and Statistics

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Prof David Bryant Applications accepted all year round Awaiting Funding Decision/Possible External Funding
Dunedin New Zealand AI & Machine Learning Applied Mathematics Data Analysis Economics Mathematics Statistics

About the Project

This project is part of a major research programme tackling one of the major open challenges in data science: how to merge existing machine learning (ML) tools, which are fast but fallible, with Bayesian approaches, which are the gold standard but computationally unobtainable. The PhD candidate would work developing new strategies and algorithms for Bayesian analysis of demographic data, working directly with researchers in academia and those in the private and public sectors.

The candidate will be working with high dimensional sampling and inference algorithms. We are looking for candidates with a strong mathematical background, and preferably experience in statistics, modelling and computation. Programming experience is essential.

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