Research Associate in Bayesian Statistics

We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of England. Attracting and retaining high-calibre people is fundamental to our continued success.

The Role
The School of Mathematics, Statistics and Physics (MSP) at Newcastle University is seeking to recruit a Research Associate in Bayesian Statistics managed by Dr Kevin Wilson, Dr Cristiano Villa and Prof. Fabrizio Leisen.

You will work on developing prior distributions to estimate the structure of the trees and the number of trees in a BART model by applying a novel methodology. A focus of the project is on building scalable and efficient computational tools to ease the implementation of the methodology.

You will be involved in all of the work packages of the project, and you will build on your experience in Bayesian Statistics to develop loss-based priors for BART models. You must have a solid background on computational methods for Bayesian inference to develop efficient algorithms for the new proposed modelling framework. Within the project, you will also perform theoretical modelling and data analysis, as well as disseminate your work through publications and presentations at scientific meetings.

You will join the growing Statistics group at Newcastle University and interact with the Statistics group at King’s College London. 

In addition to project-specific activities, you will be encouraged to pursue your own independent research and training.

We are committed to building and maintaining a fair and inclusive working environment.

The position is offered on a fixed-term basis for 23 months from the start date, or tenable until the project end date, whichever is soonest. Generous funds are available for travel, training and other support.

For all information enquiries about the position, please contact Dr Kevin Wilson (kevin.wilson@newcastle.ac.uk), Dr Cristiano Villa (cristiano.villa@dukekunshan.edu.cn) or Prof. Fabrizio Leisen (fabrizio.leisen@kcl.ac.uk).

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