Postgraduate research

EPSRC iCASE Edge Enhanced Self-Adapting Network of Digital Twins

Qualification: PhD

Department: School of Computing and Mathematical Sciences

Application deadline: 30 June 2022

Start date: 26 September 2022

Overview

Supervisors

Professor Ashiq Anjum and Professor Lu Liu

Project Description

A digital twin is a digital representation of a physical asset that spans its lifecycle, is updated from real-time data, and uses modelling, machine learning and reasoning to help decision-making. Currently it takes months and years to operationalize digital twins because the process is manual and time consuming. There is no standard theoretical model of digital twins that can be considered as a reference point for developments, optimization and deployments.  Without a sound theoretical model of a digital twin, this is a tedious task to map potential new data sources on it or propose new adaptations once the digital twin has been put into practice.  This project therefore aims to investigate a system model of digital twins that can learn from engineering, infrastructure as well as operational data and iteratively evolve over time as new data and insights become available. 

The resulting model will learn, optimize and adapt to evolving states, representing a desirable or expected functionality of a physical asset, enabling users to generate potential new scenarios as well as data that will be a realistic emulation of the real world experimental environment. 

The proposed model of the digital twin will be adapted and deployed in edge devices for performance aware model execution as well as for latency aware immersive analytics. The model will be deployed within edge devices such as digital assistants and handheld devices so that they can acquire, process and interpret data locally.

Funding

Funding

This 4-year EPSRC Studentship provides:

  • UK tuition fee waiver*
  • Annual stipend at UKRI rates: £16,062 for 2022/23
  • Research Training Support Grant

*International candidates would be required to fund the difference between the UK and International tuition fees (£16,404) themselves for the duration of their studies.

Entry requirements

Entry requirements

Applicants are required to hold

  • UK Bachelor's Degree 2:1 or better (or overseas equivalent) in a relevant subject.
  • A strong MSc in Computer Science, mathematics or electrical engineering is required. Industrial or research experience in system modelling, mathematical optimizations, distributed systems or machine learning will be a plus.
  • We will also consider outstanding candidates with an undergrad degree who possess strong industrial or research experience.
  • The candidates should have strong programming (C++/Java/Python), algorithmic (mathematical or machine learning algorithms )and  system development  skills.

The University of Leicester English language requirements apply.

Informal enquiries

Informal enquiries

Enquiries to: cmspgr@leicester.ac.uk

How to apply

How to apply

To submit your application, please use the 'Apply' button at the bottom of the page and select September 2022 from the dropdown menu.

With your application, please include:

  • CV
  • Personal statement explaining your interest in the project, your experience and why we should consider you
  • Degree Certificates and Transcripts of study already completed and if possible transcript to date of study currently being undertaken
  • Evidence of English language proficiency if applicable
  • In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available.
  • In the funding section, please state EPSRC iCASE Anjum studentship
  • In the research proposal section please provide the name of the project supervisors and project title 

Eligibility

Eligibility

Open to UK and International applicants

*International applicants will be required to cover the difference between the UK and International tuition fee (£16,404 per year) for the duration of their studies.

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