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

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 (https://www.gcu.ac.uk/cebe/research/smarttechnologycentre/projects/) 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.