Applications are invited for an exciting postdoc position in Bayesian Uncertainty Quantification for Aerospace Composites in collaboration with the universities of Bristol, Exeter, Southampton and the Alan Turing Institute as well as stakeholders such as Airbus, Rolls Royce and the Alan Turing Institute (within the remit of Data-centric Engineering)
You will be part of an exciting team of scientists which will include Mechanical Engineers, Applied Mathematicians and Statisticians to work in an ambitious project that involves integrating data from very different sources and that intends to reduce physical testing in aerospace structures by using virtual testing.
The role will involve a close collaboration with Prof. Scheichl at Heidelberg https://ganymed.math.uni-heidelberg.de/~rscheichl/ and the Data Centric Engineering Programme at the Alan Turing Institute https://www.turing.ac.uk/research/research-programmes/data-centric-engineering. It is envisaged the position will spend short periods collaborating at each of these institutions.
Job details:
Job title: CerTest Research Associate in Bayesian Uncertainty Quantification for Aerospace Composites
Fixed-term contract for two years with a possible 1-year extension
Department: Mathematical Sciences, University of Bath, United Kingdom
Salary: Starting from £33,199, rising to £39,609
Closing date: 5 January 2020
Applications on-line via university website: https://www.bath.ac.uk/jobs/Vacancy.aspx?ref=CC6943R
CerTest (https://www.composites-certest.com/) – full title ‘Certification for Design - Reshaping the Testing Pyramid’ – is a £6.9M investment by the UK Engineering and Physical Sciences Research Council (EPSRC). Research in CerTest will be conducted by a close partnership of academic institutions; University of Bristol (lead), University of Bath, University of Exeter and the University of Southampton, with strong industrial and stakeholder support from Airbus, Rolls Royce, BAE Systems, GKN Aerospace, CFMS, the National Composites Centre (NNC), the Alan Turing Institute, and with 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, and 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, and will be addressed by a world leading interdisciplinary team of academics and postdoctoral researchers, supported by a group of PhD studentships carried out in close collaborations with the industrial partners. For more information about CerTest see: https://www.composites-certest.com/
You should hold a PhD in computational statistics or applied mathematics and have substantial experience with Bayesian sampling methodologies in high dimensions (in areas such as uncertainty quantification or imaging) as well as having a broad statistical modelling knowledge.
Specific experience with structural mechanics models, design of experiments or reliability (survival) analysis would be an advantage, as would experience with different computing platforms such as R, C++, Python, and general HPC platforms.
This is a fixed-term contract for two years with a possible 1-year extension
For more details about the role please contact Dr Karim Anaya-Izquierdo (Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.) or Prof Richard Butler (Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.), however, please ensure that your application is submitted via the University website.
Please include a covering letter, detailed CV and an abstract of your PhD thesis with your application.
