Improving the prediction of overheating in complex urban dwellings

Dynamic thermal simulation techniques are often used to predict summertime overheating risks. However, the modelling methodology is unproven in the context of large multi-story buildings and the simulation results unreliable. Using multiple simulation models, parallel computing, uncertainty analysis, and empirical validation, this project will seek to develop a more robust prediction methodology.
This project will focus on the validation of complex white-box models and their application to overheating risk forecasting using empirically validated data. This research will build-upon existing research being carried out at Loughborough University in relation to overheating prediction methodologies.

The research will generate in depth knowledge and the skills needed to address one of the most pressing issues currently facing the built environment. The findings will help to inform evolving industry standards in this area, such as CIBSE TM59. The skills developed are also highly transferable and can be used to further the applicants career in a variety of fields including building performance modelling, data analytics and decision science.

Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Graduate School, including tailored careers advice, to help you succeed in your research and future career.
Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/
School/Dept accolade: We have been ranked 2nd in the UK for Building, and 10th in the UK for Civil Engineering (The Times and Sunday Times Good University Guide 2017)

Find out more:
http://www.lolo.ac.uk/

Entry requirements
We are ideally seeking applicants with good degrees (min 2:1) in computer science or data science, or strongly quantitative degrees (such as Physics, Engineering or Mathematics) with a substantial component of data analysis and programming. Previous experience in, or knowledge of, energy systems and dynamic simulation models is preferable.

The successful candidate is expected to possess the following qualities:
• Demonstrable skills in scientific or data-scientific programming (such as Python, R, C++);
• A strong interest in building performance and data science applications in the built environment;
• A strong interest in transferring knowledge and techniques across disciplines;
• Ability to use own initiative and prioritise workload;
• Good interpersonal and communication skills (oral and written);
• A high level of attention to detail in working methods. 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.

Contact details
Name: Mrs Lisa Grieve,
Email address: [Email Address Removed]
Telephone number: 01509 228540

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

Please quote reference: LoLoCDT18/01