Funding

Self-funded

Project code

COMP5371021

Department

School of Computing

Start dates

October, February and April

Closing date

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Benjamin Aziz.

The work on this project could involve:

  • Research in AI methods and IoT models and network platforms
  • Use  and possible development of software tools related to AI and IoT domains
  • Research in AI algorithms and mathematical theories

At the core of most global energy strategies nowadays is the goal of developing smart infrastructures that can harness clean energy ecosystems.  Among such smart infrastructures are Electric Vehicle (EV) support infrastructures, which aim at providing information to the vehicle drivers in relation to points of charging, traffic events and other road services.  EV support infrastructures are largely IoT-based, equipped with backend servers that collect and process often large amounts of data of high variety.  

One of the challenges facing any large-scale infrastructure is its resilience against external malicious attacks or internal failures. Resilience depends on the identification and assessment of vulnerabilities, and it is measured through well-defined metrics that can be monitored and trended over time. These are beyond the typical cybersecurity remit, characterised by 3As – Authentication, Authorisation, and Accounting. The rapidly growing big data flows between different IoT devices (charging, payment, information services etc), cloud and edge computer systems, and the intermediate third-party managed services/apps vastly expand the attack surfaces available, thus exposing the EV infrastructure to cyberthreats. This is in many ways, not a unique problem facing EVs. However, without resilient defenses and countermeasures continuously evolving in pace with emerging threats, the uptake of new mobility services will be hampered. 

This project aims at exploring AI techniques in the analysis of EV smart infrastructure data, with the purpose of providing a more robust journey experience for EV drivers.  We will focus on developing new methods of analysing cyber vulnerabilities of data flows, for example, using distributed federated learning to proactively monitor and identify abnormal behaviors in the data flows and storage systems and developing blockchain based data aggregation to improve transaction assurance in multiparty interactions. 

The project will be carried out in collaboration with Dr Erica Yang, Chilton Computing, an independent software design and engineering business specialising in AI-powered Internet of Things (IoT) systems.

Fees and funding

Visit the research subject area page for fees and funding information for this project

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

You'll need a good first degree from an internationally recognised university or a Master’s degree in computing or computing-related topics such as engineering or mathematics. In exceptional cases, we may consider equivalent professional experience and/or qualifications.  English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

How to apply

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.