Using big data to develop post-code level energy demand maps of the UK building stock.

Local authorities and others need to know where energy demand reduction campaigns will have the greatest impact. This research will use multiple data sets to construct a complex, layered data profile of the UK building stock. Techniques such as correlation clustering and choropleth mapping will be developed to extract and visualise information from this extended data-set and examine a range of large-scale energy demand intervention strategies.

This research will generate in depth knowledge and the skills needed to address important emerging issues in the built environment. 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, urban planning, 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 Doctoral College, 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/
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)

Entry requirements
We are seeking applicants with good degrees (min 2:1) in computer science or data science, or strongly quantitative degrees with a substantial component of data analysis and programming. Previous experience in, or knowledge of, the energy system is preferable but not required.

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 data science applications in the built environment sector;
• 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 method. 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, Administrator LoLo CDT, Loughborough
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 number: LoLoCDT18/05