Predicating summertime overheating risks in occupied dwellings using decision science (LoLoCDT18/01)

Application details:
Reference number: LoLoCDT18/01
PhD start date: 24/09/18
Advert closing date: 20/8/18
Interview date: tbc

Supervisors:
Primary supervisor: Dr Robert McLeod
Secondary supervisor: Prof. Kevin Lomas

Introduction:
The studentship is offered by the ESPRC, London Loughborough (LoLo) Centre for Doctoral Training in Energy Demand, which provides world-class training and supervision by academics who are leaders in their field. The Centre is based in the School of Architecture Building and Civil Engineering, which is ranked 2nd in the UK for Building and 10th for Civil Engineering (The Times and Sunday Times Good University Guide 2017). Overall, research at Loughborough is in the top ten of English universities by research intensity (REF2014).

Project Detail:
Summertime overheating is a serious and growing risk to the health of UK citizens. This project will focus on the validation of alternative modelling methods and their application to forecasting using empirical data. The research will build-upon existing research being carried out at Loughborough University and utilise machine learning and deep learning techniques.

The European heatwave of 2003 led to the premature deaths of around 15,000 people, and with climate change causing warmer summers and more frequent and intense heatwaves, deaths related to overheating could triple by 2040. The research carried out in this PhD will use machine learning techniques to develop data driven models that could be incorporated into an early warning device capable of reliably alerting facilities managers and health care professionals of impending risks.

This research will generate in-depth knowledge and the skills needed to address one of the most pressing issues currently facing the built environment. The skills developed are also highly transferable and can be used to further applicants’ careers in a variety of fields including building performance modelling, data analytics and decision science.

Find out more:
About the CDT: www.lolo.ac.uk
About the Loughborough University Doctoral College: www.lboro.ac.uk/study/postgraduate/supporting-you/research

Entry requirements:
Applicants will normally need to hold, or expect to gain, at least a 2:1 degree (or equivalent) in a relevant discipline (computer science or data science, or strongly quantitative degrees with a substantial component of data analysis and programming).

A relevant master’s degree and/or experience in one or more of the following will be an advantage: Physics, Computer Science, Data Science, Applied Mathematics, Mathematical Modelling, Building Performance Engineering. Previous experience in, or knowledge of, the energy system is preferable but not required.

The successful candidate is expected to possess demonstrable skills in machine learning or data-scientific programming (such as Python, R, C++).

Contact details:
For information about the research project, please contact:

Name: Mrs Lisa Grieve, Administrator LoLo CDT, Loughborough
Email address: [Email Address Removed]
Telephone number: (01509) 228540

How to apply:
All applications should be made online at www.lolo.ac.uk/join-us/how-and-when-to-apply/

Please quote reference number: LoLoCDT18/01