PhD Studentship: Next Generation Smooth Modelling Methods for Data Science

University of Bristol - School of Mathematics

The project:

The aim of the project is to develop new statistical computing methods for modelling large complex datasets using smooth regression models. The PhD supervisor, Prof. Simon Wood, is responsible for the `mgcv’ package for generalized additive regression modelling, which is part of the standard distribution of the R statistical computing language and environment (estimated to have in excess of 2 million users worldwide, split between academia and industry). The PhD offers the opportunity to be involved in the further development of the  package and its underlying statistical methods, by working on one of a variety of possible projects driven by real problems in modern data science. There is some flexibility to choose from a number of alternative projects according to individual student’s preferences and interests: for example projects are available in multivariate modelling, modern MCMC based inference, statistical ecology applications, diagnostics and methods for dealing with confounding and energy load forecasting. An interest in statistical computing and in producing methods of direct practical use would be an advantage.

How to apply:

Please make an online application for this project at selecting Mathematics on the Programme Choice page. When prompted in the Funding and Research Details sections of the form specify that you wish to be considered for Simon Wood DTA studentship

Additional advice on how to complete your application can be found on our postgraduate advice page.

Candidate requirements: 

A good first degree or masters in a subject with a strong statistical component.


Includes tuition fees, research travel grant and a full stipend at the EPSRC DTA rate (£14,553 2017/18) for 3.5 years.

Contacts: Prof. Simon Wood,

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South West England