The overall aim of the PhD studentship is to provide a step-change in the integration of digital twins with real-time passive sensing and human participatory sensing data streams on indoor environmental conditions to identify the best strategies for improving occupant wellbeing. The research hypothesis is that future non-domestic buildings will be inundated with cheap and reliable environmental sensors that enable facility managers and occupants to interact with the building in real-time to optimise energy efficiency and comfort level.

In this project we will create timeless digital twin stock models of our new buildings on campus that not only comprehensively represent each building, but are continually kept up-to-date using the novel integration of passive sensing and human participatory sensing.

A combination of observation and intervention based approaches will be deployed. Ultimately the project will help in enabling the identification of suitable strategies for improving the indoor environment and occupant wellbeing in non-domestic buildings in current and future scenarios.

Entry requirements

Masters degree in a related discipline (Architecture, Engineering, IT)
Experience of undertaking quantitative analysis of datasets
Experience of designing and analysing questionnaires, interviews.
Good written and verbal communication skills

Application process

When completing your application please note the following:
Title: Digital Twins for Indoor Environment
Select the following course: Architecture

Applications must be completed by 1 October 2023