Bayesian Inference for Quality Engineering

Brunel University London - College of Engineering, Design & Physical Sciences

Applications are invited for a PhD studentship which aims to develop Bayesian inference and decision making tools with applications to digitally-enabled quality engineering. The studentship is part of a collaborative project led by Brunel University London and the National Physical Laboratory (NPL). Brunel University London are supporting the development of the interdisciplinary research Institute of Innovative Quality Engineering and Smart Technology (I2QEST). The I2QUEST is an international Industry - University - Research innovation platform, recently launched with Sichuan Mingxin Investment Group (SMIG), supported by Chengdu High-tech Zone, with its vision to be a world leading Centre of Excellence in innovative quality engineering and smart technology.

Measurement plays a critical role in quality assurance in manufacturing, providing information that enables processes to be kept in control and assessing the conformance of manufactured workpieces to their specification. Traditional statistical methodologies applied in manufacturing make many pragmatic assumptions that represent idealised models of the processes. This studentship will develop more realistic models and associated statistical inference tools that reflect the actual behaviour of the processes and that make best use of the information available from measurement systems and sensors. The research will contribute to the development of national and international standards for data and quality engineering.

The student will be based jointly at Brunel’s Uxbridge campus and at NPL’s Teddington site and will be part of the research teams in Brunel’s College of Engineering Design and Physical Sciences and NPL’s Data Science group. The student will be able participate in national and international conferences, interact with industrial partners, and will have an opportunity to travel to Chengdu, China, to work with collaborators associated with the Chengdu High-Tech Zone.

For an informal general discussion regarding the above post, please contact Prof Alistair Forbes at Alistair.Fobes@npl.co.uk or Dr QingPing Yang at QingPing.Yang@brunel.ac.uk.

Eligibility Criteria
Applicants should have (or expect to obtain) a 1st class or 2:1 honours degree in in mathematics, statistics or a closely related discipline. Experience in scientific computation and/or machine learning is desirable. A Masters qualification is an advantage but not essential.

Successful applicants will receive an annual stipend of £16,558 (UK/EU candidates) or £10,000 (Overseas candidates) plus payment of their full time UK/EU or Overseas tuition fees, whichever is appropriate, for a period of 36 months (3 years).

How to Apply
Please e-mail your application comprising all of the documents listed below by Wednesday 3rd January 2018 to cedps-pgr-office@brunel.ac.uk:

  • Your up-to-date CV.
  • A 300-500 word document setting out ideas for your research project.
  • A one A4 page statement setting out why you are a suitable candidate (i.e. your skills and experience).
  • A copy of your highest degree certificate and transcript.
  • Evidence of English language skills to IELTS 6.5 (or equivalent), if appropriate.
  • Two academic references.

Interviews will take place during January 2018 with a proposed start date of 1st February 2018.

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Type / Role:

PhD

Location(s):

London