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Fully-Funded Doctoral Studentship in Artificial Intelligence in Engineering Management in the Manufacturing Environment; Department of Engineering, Durham University

Durham University - Department of Engineering

Qualification Type: PhD
Location: Durham
Funding for: UK Students, EU Students
Funding amount: £15,009
Hours: Full Time
Placed On: 3rd June 2019
Closes: 15th July 2019

Funding for:UK Students, EU Students

Funding amount:

Duration: 3.0 years

Fully-Funded Doctoral Studentship in Artificial Intelligence in Engineering Management in the Manufacturing Environment; Department of Engineering, Durham University

Supervisors: Stefano Giani, Oliver Vogt

Durham University is seeking applications for a PhD studentship as part of our ERDF funded project Artificial Intelligence in Engineering Management in the Manufacturing Environment. This project is a collaboration between the Universities of Durham and Dyer Engineering The successful applicants will be working alongside industrial experts from Dyer Engineering. They will be able to access training through the University’s Researcher Development Programme.

Project Title

Artificial Intelligence in Engineering Management in the Manufacturing Environment

Project Description

The focus of this project is concerned with the development of novel AI solutions to aid the quotation process.

Dyer Engineering is a rapidly expanding business which manufactures metal components and structures and delivers related services. Their business operates across a diverse range of markets, with the ability to manufacture parts you can pick up by the handful, through to large-scale structures operating in harsh sub-sea environments. They also provide support services to customers ranging from paper mills to aerospace companies.

The research of this AI project will be focused around the need to create a ‘feedback loop’ of continual improvement of enquiry prioritisation, quotation accuracy (plan vs actual), commercial base costing verification and automated review, shop floor communication and production process optimisation. In other words, the proposed project aims at investigating the quotation process to neither reduce profit nor lose business due to unrealistic quotations.

Assessment Criteria

Prospective candidates will be judged according to how well they meet the following essential criteria:

  • At least an upper second class honours degree in engineering, physics, the mathematical sciences or computing sciences.
  • Strong understanding of engineering applications and problem solving.
  • Excellent written and spoken communication skills in English.
  • Good programming skills desirably in python.

The following criteria are desirable but not essential;

  • Knowledge of artificial intelligence and machine learning, relevant to the particular project.

Candidates should contact the relevant member of academic staff to discuss project-specific requirements.

 Funding and Application Process

  • The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting gender and ethnic diversity in Science, Engineering and Technology.
  • Decisions will be made on applicants as they are received.
  • The academic supervisors, Stefano Giani ( or Oliver Vogt (, are available for informal discussions with any interested candidates who would like further details before submitting an application.
  • To apply formally for this studentship, applicants should submit an application using the online system found at
  • The UK/EU studentships are fully funded for 3 years with a tax-free stipend at ERDF rate (£15,009 for 2019/20).

Closing Date for Applications: 15/07/2019

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