University of Exeter funding: Smart and holistic district level e

Smart and holistic district level energy management systems underpinned with blockchain, optimisation, federative cloud and artificial intelligence, Engineering – PhD (Funded) Ref: 3908

About the award


Academic Supervisors:

  • Dr Baris Yuce, College of Engineering, Mathematics and Physical Sciences,  University of Exeter
  • Dr Dibin Zhu, College of Engineering, Mathematics and Physical Sciences, University of Exeter

Location: Engineering Department, in the College of Engineering, Mathematics and Physical Sciences at Streatham Campus in Exeter.

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully-funded PhD studentship to commence in September 2020 or as soon as possible thereafter.  For eligible students the studentship will cover UK/EU/International tuition fees plus an annual tax-free stipend of at least £15,285 for 3.5 years full-time, or pro rata for part-time study.  The student would be based in the College of Engineering, Mathematics and Physical Sciences at Streatham Campus in Exeter.

Project Description

High Power Computing (HPC) systems are one of the most essential tools in the fields of digitalised environment like Industry 4.0 and smart cities driven systems.  HPC systems play very key roles in these fields to provide infrastructures for data collection and monitoring systems, big data analytics and machine learning solutions, communication with other cloud solutions/HPCs in near real time that are underpinned with case scenarios and optimisation algorithms, data visualisation frameworks,  IoT interaction, and actuations, and digital twins developments for the cyber-physical systems.

Last two decades, various numbers of enhancements and developments have been shown in the HPC world, especially, ontological based data modelling, holistic knowledge presentation federative cloud-based solutions, and other big data analytics. However, the recent applications are mainly focusing on intelligent system based proactive solutions, integrating with augmented reality to provide effective, adaptive, user-friendly and interactive data post-processing environments for the smart cities, industry 4.0 applications. Hence, the new research directions in this field will focus on to generate intelligent and adaptive solutions that deal with uncertain, incomplete data, engage with other cloud services efficiently, with the holistic approach.

This research project will tackle one of the key problems in the smart city, district level energy management, to address the demand side energy management for the districts using multi-mode predictive and optimised scenario-based solutions in a holistic federative cloud infrastructure. The district levels energy management problems are combination of several hierarchical problems such as multilevel and multi vector energy generation systems including thermal, electrical energy resources from national grid and onsite renewable resources, and multi-level and multi vector energy consumers.

To be able to develop efficient control strategies and solutions for such complex problems, HPC development is essential, in addition, data modelling process should demonstrate an ontology-based data modelling solution that underpinned several artificial intelligence technologies. In addition, the optimisation and prediction technologies should consider uncertainty and multi-mode consumption and generations. In addition, the proposed solution should also consider the blockchain based energy market systems that manage the trading between consumers and prosumers. The proposed user interface for such solution should also be a holistic and novel solution that utilises a strong game engine technology which provides an interactive solution to the users and empowered with HTML5 technologies. The solution should also be a representation/ digital twins of the cyber-physical world.   Hence the solution can be extended urban level to develop a near real time solution for the smart grid.

Entry requirements

Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology Experience in Engineering and/or Computer Science is desirable. Excellent programming skills in Java, JavaScript, MATLAB, C/C++ and Python. Published articles/papers are preferable option. Good understanding in microcontrollers and sensory based infrastructure. Good knowledge in HPC/cloud and web-service developments. Good understanding in data visualisation using game engines. and are desired skills. Good understanding in optimisation problem modelling and solutions. The candidate should also have good understanding in artificial intelligence technologies and their applications in smart city, industry 4.0 fields.

If English is not your first language you will need to have achieved at least 6.5 in IELTS and no less than 6.0 in any section by the start of the project.  Alternative tests may be acceptable (see

How to apply

In the application process you will be asked to upload several documents. 
• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Research proposal
• Published articles.
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to quoting the studentship reference number 3908.
• If you are not a national of a majority English-speaking country, you will need to submit evidence of your proficiency in English.

The closing date for applications is midnight on 15th June 2020.  Interviews dates are to be confirmed.

If you have any general enquiries about the application process please email or phone +44 (0)1392 725801 or +44 (0)1392 725150.  Project-specific queries should be directed to the main supervisor.


Application deadline:15th June 2020
Duration of award:per year
Contact: STEMM PGR Admissions