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Smart energy, technology and people (HARGREAVEST_U20MCITN)

   School of Environmental Sciences

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  Dr T Hargreaves, Dr C Wilson  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Smart technology is everywhere - in our homes, pockets, and networks. Is smarter use of energy essential for low-carbon energy systems of the future? Or is smartness just a buzzword for new gadgets that require ever-more energy and worsen the digital divide? Smart technology is a mass of hopes, fears and contradictions.

This PhD offers a unique opportunity to untangle relations between smart energy, technologies and people, supported by a major new EU-wide PhD training network. This interdisciplinary ‘GECKO’ network connects leading social, computer and data scientists working on smart technology, artificial intelligence, human-computer interaction, energy, climate change, and responsible innovation.

The successful applicant will benefit from a wealth of training opportunities, from secondments to intensive summer schools, as part of a cohort of 15 PhDs networked across Europe. The successful applicant will also have access to a substantial research budget as well as generous living and mobility allowances and family allowance (equivalent to an annual salary of €62,052). This cohort will form the next generation of applied scientists helping to advance knowledge on pressing policy and technology development issues in the rapidly changing field of smart technology, energy and people.

The successful applicant may tackle questions such as:

(1) In what ways should algorithms steer energy users towards socially-beneficial outcomes?

(2) What are the social justice implications of smart technologies?

(3) How responsive are different domestic activities to control by smart technologies?

(4) What are the benefits and risks of smart technologies for energy-poor households?

The supervisors welcome discussions around these and related themes, so the successful applicant can shape their PhD research in line with their own interests, while maximising the benefit of the training and skills-development opportunities of the GECKO network.

All applicants should have a good Masters degree (or equivalent) in an applied social science discipline such as human computer interaction, geography, sociology, design, psychology or statistics. All applicants should be familiar using both quantitative and qualitative research methods.

For more information on the supervisor for this project, please go here 

Type of programme is a PhD

Start date: 1st June 2021

Mode of Study: Full Time

Studentship length: 36 months NB. 3 year studentships have a (non-funded) 1 year ‘registration only’ period

Entry Requirements: Applicants should have a good Masters degree (or equivalent qualification). 

Funding Notes

This project is awarded with a 3-year PhD scholarship through the Horizon 2020 Marie Skłodowska-Curie Actions - Innovative Training Networks (ITN) Programme (No. 860635, pending Grant Agreement Signature). Applicants are eligible to apply who have not been based in the UK for more than 12 months in the last 3 years. Remuneration will be in line with the Marie Skłodowska-Curie guidelines (Early Stage Researchers, ITN). For programme, funding information and eligibility criteria please visit:
Tuition fees are covered for home rate only (£4,407 2020/21 rate). A living salary will be provided for each year of the scholarship.


i) Hargreaves, T., & Wilson, C., 2017. Smart Homes and Their Users. Springer, Cham, Switzerland.
ii) Hargreaves, T., Wilson, C., & Hauxwell-Baldwin, R., 2017. Learning to live in a smart home. Build. Res. Inf. 46, 127–139.
iii) Wilson, C., Hargreaves, T., & Hauxwell-Baldwin, R., 2015. Smart homes and their users: a systematic analysis and key challenges. Pers. Ubiquitous Comput. 19, 463–476.
iv) Wilson, C., Hargreaves, T. & Hauxwell-Baldwin, R., 2017. Benefits and risks of smart home technologies. Energy Policy. 103, 72-83.
v) Stankovic, L., Stankovic, V., Liao, J., & Wilson, C., 2016. Measuring the energy intensity of domestic activities from smart meter data. Applied Energy. 183, 1565-1580.
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