PhD in Statistics in Bayesian Methods

Applications are invited for a three year postdoctoral research position to work on a multidisciplinary research project "Simulation Tools for Robust Automated Manufacturing". The project is a new collaboration between statisticians at the School of Mathematics and Statistics and engineers at the Advanced Manufacturing Research Centre at the University of Sheffield.
The project objective is to develop and test a methodological framework for efficient optimisation of automated manufacturing processes that will be robust to input uncertainties. The main job roles will be to

1. develop and implement methodology for the Bayesian analysis of uncertainty in computer model outputs, with a particular focus on model calibration and optimisation, and

2. develop and implement statistical methodology for signalling out of control processes in the presence of autocorrelation. Use this methodology to construct optimal feedback adjustment techniques, in order to return the processes to target levels.

The Research Associate will be working under the supervision of Dr Jeremy Oakley, Dr Eleanor Stillman and Dr Kostas Triantafyllopoulos in the School of Mathematics and Statistics, and will be collaborating actively with researchers at the Advanced Manufacturing Research Centre.

Candidates should have (or expect soon to have) a PhD in Statistics or a related field with a strong background in Bayesian statistical methods, and good statistical computing skills in a software environment such as R or Matlab.

This post is fixed-term with a start date of July 1st 2013 and an end date of June 30th 2016.


For further details, including information about how to apply, go to http://www.shef.ac.uk/jobs/ and search for this job using the reference number UOS006589



For informal enquiries, please contact Jeremy Oakley (j.oakley@sheffield.ac.uk) +44 114 2223853