|Salary:||The starting salary for Associate will be from £32,411 on Grad E. For Fellow it will be from £40,745 on Grade F, depending on qualifications and experience.|
|Placed On:||28th March 2023|
|Closes:||28th April 2023|
Statistical Machine Learning Methods for Aerospace
This full time post is available immediately on a fixed term basis until 31 July 2024 (with the possibility of extension beyond this, subject to approval). This role offers the opportunity for hybrid working – some time on campus and some from home.
Summary of the role
The position is based within the Institute of Data Science and AI at Exeter working with Professor Tim Dodwell. The position will spend time working at and with the Alan Turing Institute in London and IWR Heidelberg.
This position is part of the wider programme CerTest (full title 'Certification for Design - Reshaping the Testing Pyramid') with £6.9 million research investment from the UK Engineering and Physical Sciences Research Council (EPSRC). CerTest is conducted in close partnership between academic partners University of Bristol, University of Bath, University of Exeter and the University of Southampton, with strong industrial and stakeholder support by Airbus, Rolls Royce, BAE Systems, GKN Aerospace, CFMS, the National Composites Centre (NNC) and the Alan Turing Institute, and close interaction with the European Aviation Safety Agency.
This position will:
(1) Develop statistical machine learning methods to build efficient, validated, surrogates of high-performance composite simulations.
(2) Focus on efficient building machine learning approximations which are scalable to large input and output dimensions.
This role will provide the opportunity for new method development, mathematical theory and large scientific calculations, within an application of significant industrial importance. You should hold a PhD (or nearing completion for Associate role) in Applied Mathematics or Computer Science. Strong programming skills in python, with knowledge of major machine learning libraries is essential. Knowledge of Bayesian methods or Stochastic Machine Learning and/or experience of solving mechanics problems will be a distinct advantage.
The role will involve a close collaboration with Prof. Scheichl at Heidelberg and the Data Centric Engineering Programme at the Alan Turing Institute. It is envisaged the position will spend extended periods collaborating at each of these institutions.
Please ensure you read the Job Description and Person Specification for full details of this role.
Please contact Professor Tim Dodwell, T.Dodwell@exeter.ac.uk.
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