EMPLEO | Research Fellow in Statistics, University of Southampton and Alan Turing Institute

University of Southampton and Alan Turing Institute

Salary: £30,942 – £34,804
Full Time Fixed Term for 12 months
Closing Date: Friday 13 August
Start date: October 2021
Reference: 1439821PJ

Applications are invited for a 12-month position of Research Fellow in Statistics at the University of Southampton and the Alan Turing Institute as part of the EPSRC-funded “CerTest” project.

The successful applicant would be based at Southampton but have the opportunity to spend substantial time (physically or virtually) at the Turing Institute in London as a member of the Data-Centric Engineering programme. The postholder will collaborate extensively with statisticians and mathematicians from the Universities of Bath and Exeter, and Engineers from the Universities of Bath, Bristol and Southampton.

The postholder will work on methods for uncertainty quantification in complex computer experiments for materials engineering. In particular, on one of more of the topics of (a) multi-model emulation and design of experiments (propagating uncertainty through linked computer models); (b) statistical calibration, and related design of experiments, for population parameters in mixed-effects computer models; (c) high-dimensional emulation (inputs and outputs). There will be opportunities to apply the methods to experiments performed by collaborating engineers, and to gain insight into their practical importance and relevance via the projects industrial partners.

CerTest – full title ‘Certification for Design – Reshaping the Testing Pyramid’ – is £6.9M research investment (programme grant) of the UK Engineering and Physical Sciences Research Council (EPSRC). CerTest is a close partnership between academic partners at the Universities of Bristol (lead), Bath, Exeter and Southampton, with strong industrial and stakeholder support from Airbus, Rolls Royce, BAE Systems, GKN Aerospace, Centre for Modelling and Simulation, the National Composites Centre and the Alan Turing Institute, and close interaction with the European Aviation Safety Agency. CerTest addresses barriers to validation and certification of composite aerostructures posed by the so-called ‘building block approach’ (or ‘testing pyramid’), which is the backbone of current validation and certification processes. CerTest represents a decisive step towards ‘virtual testing’ on the structural scale, which aims to reduce development cost and time to market, as well as to enable more structurally efficient and lightweight composite aerostructures that are essential for meeting future fuel and cost efficiency challenges.

The core research challenges in CerTest are strongly multidisciplinary. The research is conducted through close collaboration between the universities and with the industrial partners by a team of academics and postdoctoral fellows, supported by a group of PhD and EngD studentships.

For more information about CerTest see: CerTest – Certification for Design: Reshaping the Testing Pyramid

To deliver the overarching objectives of CerTest it is necessary to develop methodologies that enable high-fidelity and robust testing of composite aero-structures on the component and sub-structure levels. A key part of the novel research will be to design the testing approach and to integrate the physical testing with multi-scale modelling in a closed feed-back loop, so that the models inform the choice of physical tests and the test outputs inform and update the models.

This role will require a combination of technical and scientific skills, and the ideal candidate should hold a PhD in Statistics, relevant areas of Applied Mathematics or quantitative areas of Engineering. Experience in Bayesian modelling and computation and/or design of experiments would be highly desirable, along with experience in and/or enthusiasm for engineering applications and computational modelling.

This post is being offered on a full-time basis for 12 months starting as soon as possible after 4 October 2021.
Informal enquiries are encouraged and may be made to Professor David Woods (email: D.Woods@southampton.ac.uk).