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Advanced machine learning techniques for IoT and smart systems

   Faculty of Natural Sciences

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  Prof Z Fan  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Internet of Things (IoT) is a key enabling technology for various smart system applications in different industry sectors, e.g. energy, transport, healthcare, logistics. On the other hand, AI and machine learning (ML) has made tremendous progress in the past few years in addressing some of the most significant technical challenges in our society. This project will provide an opportunity to apply advanced ML techniques (e.g. federated learning, transfer learning, deep reinforcement learning, etc.) to IoT applications in different verticals and an opportunity for substantial contribution to international publication in leading journals and conference proceedings. The PhD research will involve the use of ML and data analytics algorithms as well as hands on opportunities with IoT systems and devices.

Keele University is renowned for its exciting approach to higher education and research, beautiful campus, strong community spirit and excellent student life.

The University has the UK’s largest campus with 617 acres of landscaped parkland, fields, woodlands and lakes. Keele University runs its own day nursery for infants from 3 months to 5 years and is committed to equality and diversity.

Information for prospective postgraduate researchers can be found here:

Research Context

This PhD project will connect with the SEND project at Keele, which is one of the largest living labs on smart energy in Europe.

 The research will be supervised by Prof. Zhong Fan in the School of Computing and Mathematics at Keele University and, potentially, with other academic and industrial partners.

Applications are welcomed from science, technology, engineering or mathematics graduates with (or anticipating) at least a 2.1 honours degree or equivalent. Applicants should have good computing skills, an enthusiasm for IoT and AI but will not require specific expertise or experience in this area. Applicants should have an enthusiasm for applying advanced algorithms to solve practical problems as well as a willingness to acquire new skills. Ideally, applicants will be self-motivated and have the ability to work both independently and as part of a team.

Open to fully self-funded students only (please see funding notes).

This opportunity is open to UK/EU and overseas students. The collaborative and presentation aspects of the research require good English language and communication skills. Overseas applicants would therefore require an English IELTS (or equivalent) of 6.0 overall with no less than 5.5 in any subtest.

Informal enquiries about the project are very welcome by email to the Project Lead, Prof. Zhong Fan ([Email Address Removed]).

Full applications should be submitted to:

Please click "Apply for PhD" Please state FNS 2021-04 on your application.

Keele University values diversity, and is committed to ensuring equality of opportunity. In support of these commitments, Keele University particularly welcomes applications from women and from individuals of black and ethnic minority backgrounds for this post. The School of Computing and Mathematics and Keele University have both been awarded Athena Swan awards and Keele University is a member of the Disability Confident scheme. More information is available on these web pages:

Funding Notes

Open to fully self-funded students only.
Please note that self-funded applicants must provide funding for both tuition fees and living expenses for the 3 year duration of the research. There is a future possibility of competitive scholarship awards for outstanding applicants (1st class honours), however, none are currently available. For information regarding University tuition fees please see:
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