The UK has an abundance of data on housing, energy use, and health. Cutting edge data science and housing stock models will be used to explore the complex inter-relationships between home energy efficiency, fuel poverty, indoor temperatures, and health.
The UK must rapidly retrofit its building stock to achieve net-zero targets by 2050. Improving the energy efficiency of homes is more urgent than ever in the face of cost-of-living and climate crises. The number of UK households in fuel poverty has almost doubled since 2020 according to National Energy Action (NEA). Each year in England and Wales, there are on average nearly 800 excess deaths associated with heat and over 60,500 associated with cold. With an aging population and increases in extreme weather events, these values are likely to increase. UK housing must become more efficient and better adapted to provide thermally comfortable conditions in both summer and winter. This project will help inform government policy on home energy efficiency programs and the provision of fuel subsidies.
Studentship aims:
The aim of this project is to improve our understanding of the complex inter-relationships between UK housing characteristics, indoor temperatures, energy consumption, fuel poverty and health (related to heat and cold exposure). The main objectives are to:
- improve the predictions of an existing UK housing stock model;
- derive relationships between outdoor and indoor temperatures for UK homes by location, dwelling characteristics, and occupancy using multivariate techniques;
- link housing data with administrative health records (mortality and/or morbidity) at high spatial resolution to estimate the strength of associations between dwelling characteristics, indoor heat/cold exposures, energy use and health outcomes across socioeconomic groups.
Person specification:
The ideal candidate will come from a mathematical, physical or data science background and have knowledge of statistical and machine learning methods. Some prior experience of python (or other statistical programming language) and building simulation is desirable but not essential.
A minimum of an upper second-class UK Bachelor’s degree and a Master’s degree, or an overseas qualification of an equivalent standard, in a relevant subject, is essential. Exceptionally: where applicants have other suitable research or professional experience, they may be admitted without a Master’s degree; or where applicants have a lower second-class UK Honours Bachelor’s degree (2:2) (or equivalent) they must possess a relevant Master’s degree to be admitted.
Applicants must also meet the minimum language requirements of UCL
Applicants should be familiar with the changes to EU and International Eligibility for UKRI funded studentships.
Dates: 4 years from September 2023
How to apply
All CV’s and Cover Letters must be completely anonymised and not contain any references to protected characteristics, such as gender, ethnicity or race.
Please submit your application by email to the UCL ERBE Centre Manager ([Email Address Removed]) with Subject Reference: 4-year PhD studentship in ‘Data-driven techniques for healthy, energy resilient housing’
The application should include each of the following:
- An anonymised Cover Letter clearly stating why you are applying and how your interests and experience relate to this project, and your understanding of eligibility according to these guidelines: EU and International Eligibility for EPSRC/UKRI funded studentships
- An anonymised CV
- Complete the CDT EPSRC Eligibility Questionnaire and EDI Questionnaire via the linked Microsoft Forms.
Only shortlisted applicants will be invited for an interview.
- For the interview shortlisted candidates will be asked to show proof of their degree certificate(s) and transcript(s) of degree(s), and proof of their fees eligibility.
- The interview panel will consist of the project’s academic supervisors at UCL and a representative of the ERBE CDT Academic management. The interview will include a short presentation from the candidate on their ideas of how to approach this PhD project.
Following the interview, the successful candidate will be invited to make a formal application to the UCL Research Degree programme for ERBE CDT.
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