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Fully Funded EPSRC PhD Scholarship: iCASE Artificial Intelligence and Machine Learning for MRI Magnet Manufacture

  • Full or part time
  • Application Deadline
    Monday, June 08, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

This scholarship is funded by EPSRC CASE Award with Siemens Healthineers Magnet Technology.

Start date: October 2020

Subject areas: Computational Engineering, Data Science, Artificial Intelligence

Project description:

Subject areas: Computational Engineering, Data Science, Artificial Intelligence, Big Data, Neural Networks

Project description:

Artificial intelligence (AI), machine learning (ML), and the broader umbrella of (big) data science, are buzzwords and have helped contribute to the global successes of companies like Google and Amazon. The data they collect using our Internet searches and Voice assistants (e.g. Alexa) are used to make predictions about our likes and dislikes, to make recommendations to us, to sell us products and services as well as to improve the efficiency of their own businesses. Given this success, it is no surprise that AI and ML algorithms (e.g. neural networks) have attracted the interest of engineering companies across the globe in order to use data they are already collecting to improve their products, their manufacturing processes and the services they provide to their customers.

This CASE award EPSRC PhD project will focus on AI/ML with the scope to make a real impact on the manufacturing process of a global leading company in the manufacture of MRI scanners, namely Siemens Healthineers.

The student will learn about the latest developments in AI/ML and apply these techniques to understand and improve the magnets, which form an integral part of the Siemens Healthineers’ MRI scanners. The aim is to understand which manufacturing processes, or circumstances, lead, in exceptional circumstances, to magnets that behave outside the normally expected performance parameters. Identification of suspected sensitivities in the magnet build from the ML analysis will be expected to be rolled in to changes within the factory. The successful applicant will be able to have a direct impact on the performance of MRI scanners produced by Siemens Healthineers.

This will be achieved through the following objectives:

To understand and to be able to apply the latest AI/ML algorithms to data science problems
- To gain a good understanding of the physical engineering processes in the manufacture and operation of MRI magnets.
- To work effectively with software libraries, big data sets and to present results in an informative manner
- To be able to compare the performance and access the quality of AI/ML predictions
- To make recommendations based on AI/ML predictions to influence the design cycle of MRI magnets
- To communicate ideas and results clearly on paper and in presentations to industrialists and the academic community

Eligibility
Candidates should hold a minimum of an upper second class (2:1) honours degree (or its equivalent) in Engineering, Mathematics or similar relevant science discipline including physics, computer science.

The ideal candidate will have a strong interest in AI/ML/Data science coupled with an excellent engineering/physics background. Candidates with additional experience, for example in the form of a master’s degree or a year industry, are very much encouraged to apply.

We would normally expect the academic and English Language requirements (IELTS 6.5 overall with 5.5+ in each component) to be met by point of application. For details on the University’s English Language entry requirements, please visit – http://www.swansea.ac.uk/admissions/english-language-requirements/

Due to funding restrictions, this scholarship is open to UK/EU candidates only (EU nationals are required to be ordinarily resident in the UK for at least 3 years prior to the start of the scholarship).

Funding Notes

This scholarship covers the full cost of UK/EU tuition fees and an annual stipend starting with £ 18,285 in year one rising to £ 22,221 in year four.

There will be an additional £10,000 available for project costs.

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