UCL Energy Institute


New PhD studentship in Smart Meter Demographics

13 July 2020

The new 4-year PhD studentship is funded by EPSRC and EDF (CASE), and starts September 2020.

Photo shows a smart meter

Funded PhD studentship in Smart Meter Demographics

Funded by EPSRC and EDF (CASE).

Primary supervisor

Dr Aidan O’Sullivan, UCL Energy Institute

Secondary supervisor

Prof Tadj Oreszczyn, UCL Energy Institute


The studentship will cover UK course fees and an enhanced tax-free stipend of approx. £17,285 per year for 4 years along with a substantial budget for research, travel, and centre activities. Applicants should meet the EPSRC eligibility criteria.


4 years starting Autumn 2020


In addition to UCL's academic requirements for admittance to Doctoral studies, see the following additional ones on the EPSRC website. https://www.epsrc.ac.uk/skills/students/help/eligibility/

About the studentship

The national deployment of smart meters in the United Kingdom, which record high resolution electricity consumption data, presents an opportunity to develop a new wave of innovative energy solutions that leverage this data for a more efficient, sustainable and affordable future. Energy consumption data is extremely diverse and heterogeneous in composition. It is a function of peoples’ habits, appliances, dwelling properties, the weather and many other factors, known and unknown. To better understand the underlying invariants and aggregate patterns in this consumption domain, a whole new ‘demographics’ of smart meters is needed.

Studentship aims

The objective of this PhD is to generate a new smart meter data ‘demographics’ in order to optimize the use of smart meter data and enhance insight gathered from it, using advanced data analytics and modelling techniques. The research contribution will cover a wide spectrum, from the establishment of a theoretical framework to its demonstration on concrete cases provided by the industrial sponsor EDF. A substantial part of the work will consist of applying advanced machine learning techniques & Artificial inteligence (AI) to cluster, summarize and model smart meter data, alongside other data sources (demographics, census, weather, locations, etc.).

Person specification

Candidates should have:

  • A bachelor’s degree with a minimum of upper second-class (2:1) honours, or an overseas qualification of an equivalent standard.
  • A Master’s degree in statistics, engineering, computer science or other relevant disciplines.  Candidates without a Master’s degree may be admitted where suitable experience is demonstrated.
  • Expert knowledge of machine learning and artificial intelligence methods.
  • An understanding of the energy system and the role of suppliers
  • Excellent numerical and computing skills.
  • Excellent interpersonal and communication skills (oral and written).

Application procedure

How to apply

Applicants should access the UCL SELECT portal.

Scroll to the bottom of the page and click the ‘Apply now’ button.

The deadline for applications is: 09.00 am (BST), Tuesday 4 August 2020.

Please include:

  • A covering letter,
  • CV,
  • Names and addresses of two academic referees,
  • Copies of your degree certificate(s) and transcript(s),
  • A short research proposal (1000 words).

For informal enquiries on the research topic or your eligibility, email Dr Aidan O’Sullivan with ‘Smart Meter Demographics’ in the subject field.

Interviews will be held online during the week of 17 August 2020.