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Digital Twins and big data for societal challenges and for the Common Good (i.e. electricity, healthcare, manufacturing, built environment, construction)

   School of Computing, Engineering & the Built Environment

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  Prof H Larijani, Prof Anjali De Silva, Prof H Tianfield  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Please refer to project number SCEBE/22S/001/HL

This project is available as a 3 years full-time PhD study programme with expected start date of 1 October 2022 

Digital twins are virtual replicas and representations of assets, processes, systems, or institutions in the built, societal, or natural environments. They provide real-time insight into how complex physical assets and citizens behave, helping organisations improve decision-making and optimise processes. Digital twins fundamentally differ from computer models as they can provide significant amounts of real time data, allowing real time interaction with the physical twin.

Digital twins can address societal challenges such as sustainable engineering, urban environments or healthcare and aid the development of solutions (e.g. smart distribution power networks, smart remote health monitoring, Industry 4.0, Smart Cities, Smart buildings, Smart Construction and Manufacturing).

Building on work already completed or currently underway at the SMART Technology Centre ( and in collaboration with the School’s research centres in Built Environment Asset Management and Climate Justice, this interdisciplinary project aims at investigating and developing solutions for a societal challenge (e.g. sustainable engineering/construction/healthcare etc.) using this technology and big data.

The successful candidate will have an Engineering (Power, Electronics, Manufacturing, Mechanics, Telecommunications, etc.)/ Computer Science or Computing / Construction or Built Environment degree and/or data science background (First Class or 2:1 Honours) and a Master’s degree (ideally at least Merit) in a related area or at the interface between the two (e.g. AI, Machine Learning or Engineering, Computer Science, Health sciences). They will have experience of neural network techniques or willing to learn it as well as experience and knowledge of some quantitative research methods. Prior work in digital twins is desirable.

Candidates must include an outline of their ideas for exploring big data/machine learning approaches to use digital twins for solving societal challenges, drawing on relevant literature (via the ‘research proposal’ section of the application form; maximum of 750 words excluding references.

Applicants shortlisted for the PhD project will be contacted for an interview within four weeks from the closing date.  

Funding Notes

A range of funded studentships and fees only scholarships are available to the best candidates. 
For students commencing their studies in 2022/23:
The studentship is worth £20,400 per year for three years. The studentship covers payment of tuition fees (£4,560 for Home/RUK students or £15,700 for EU/International students) plus an annual stipend of £15,840 for Home/RUK students or an annual scholarship of £4,700 for EU/International students.
EU/International candidates of outstanding calibre may be awarded a studentship of £31,540 per year for three years. The International Enhanced Scholarship covers payment of tuition fees (£15,700) plus an annual stipend of £15,840.


Please address any queries on suitability etc to:
Name: Prof Hadi Larijani
GCU Research Online URL:
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