Building design and performance has a vital impact on the occupants’ productivity, health and wellbeing, and therefore directly linked to the economic cost of the organizations and companies.
The traditional engineering-based indoor environmental design and operation fails to capture the diverse needs of people in the building, address the design-performance gap, and consider the real-time feedback from occupants and provide interventions. The data-driven approaches such as using AI-based diagnostics and algorithms offer advanced analytical techniques capable of understanding and modelling the complex and non-linear nature of the interaction between occupants and their micro-environments in the workplace. This project will apply the novel data analytical approaches such as AI and ML to design healthy buildings.
Applicants are expected to have a very good bachelor’s or master’s degree in the subjects of mechanical engineering, building service engineering, environmental engineering, control engineering or related subject with research interests in the application of artificial intelligence techniques to building-related research. Basic coding skills are essential and some knowledge on mathematical modelling/machine learning is desirable. The PhD student will be supervised by Dr Zhiwen Luo (Building environmental engineering at the University of Reading) and Dr Zhan Shu (Control engineering at the University of Alberta, Canada). This project is potentially funded by University international studentship, subject to a competition to identify the strongest applicants.Apart from this studentship, there is addional PhD studentship specially for Indian citizen with excellent research background.