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BBSRC Industrial CASE PhD Studentship: Autonomous Quality Control in Manufacturing

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (UK Students Only)
    Funded PhD Project (UK Students Only)

Project Description

In collaboration with BBSRC and Nestle Global we offer a PhD studentship in Autonomous Quality Control in Manufacturing. Robust quality control is of paramount importance in the manufacturing sector in general. Relevant to quality control, specific challenges in food manufacturing include the sparse opportunities for direct product sensing, need for low cost solutions, and frequent use of expert knowledge (humans) to drive any process optimisation/control.

The very high volume – low value (VHV-LV) production, in combination with high product variability, also presents an additional challenge: to provide solutions that can be generalised and applied to a variety of products.

This PhD will focus on the creation of new methods in Computational Intelligence, specific to autonomous quality control systems for VHV-LV production in food manufacturing with high product variability. The following underpinning research themes will be addressed:
•Indirect sensing and sparse data modelling (vibro-acoustic sensing, sonar-based NDT)
•Model-based approaches for novelty detection in spatiotemporal data
•Real-time model-based autonomous decision making (multi-objective optimisation and decision making)
•Human-Centric autonomous systems (capturing, quantifying and using expert human knowledge for process optimisation)

The applicant will have an excellent first degree in manufacturing, computing, control or systems engineering, or in another relevant discipline, and be strongly numerate. You will be an excellent communicator in both written and spoken English. Above all we are seeking a highly motivated and numerate candidate with a strong interest in technology and drive to explore, learn and apply their engineering skills to autonomous manufacturing systems. Previous experience in Computational Intelligence, Modelling and Simulation, Data mining, would be an advantage.

Nestlé is the world’s largest food manufacturer and the leading Nutrition, Health and Wellness Company. Based in York, Nestlé’s Product Technology Centre (NPTC) is part of a global network of Research Centres, with the York site focusing on excellence in confectionery worldwide. Whether chocolate, sugar confectionery or wafer-based treats, the NPTC leads the way in how they’re developed, processed and packaged. It’s a place where creativity meets practicality, but where being pragmatic doesn’t hinder ideas. If anything, it calls for more expertise and ingenuity to make fantastic initial concepts become exceptional industrialised ideas.

As an NPTC PhD student you will be part of a community of 15-20 students based at top UK and overseas universities. To complement your university-based research training we offer you soft skills and industrial training that will enhance your readiness for employment. As part of this training we offer 6-12 months of placements at our centre in York spread over the course of your PhD.

Funding Notes

This Studentship will cover tuition fees at the UK/EU rate and also provide a tax-free stipend at £17,884 for the duration of the project.

Full grant requires UK Residency status: please see:View Website

References

To apply please submit a PhD application using our online application system via the Apply link at the following: http://www.sheffield.ac.uk/postgraduate/research/apply/applying

Your application should include a CV containing the names and addresses of two referees, along with a 150-word (maximum) description of your relevant experience for this research programme. You should indicate this project and enter Dr George Panoutsos as your proposed supervisor in your application. If you wish to discuss any details of the project informally, please contact Dr George Panoutsos as above.

How good is research at University of Sheffield in General Engineering?

FTE Category A staff submitted: 21.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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