Title: A distributed and real-time learning framework for Smart Meter big data
Funding: Full time Home/EU fees and a stipend of £15,009 p.a. (terms & conditions)
Application deadline: 3 July 2019
Start date: October 2019
Duration: 3 years (full time)
Location: Colchester Campus
Based in: Department of Mathematical Sciences
More than 50 million smart meters are expected to be installed by 2020 in the UK. Smart meters could record high resolution energy consumption data, which will provide enormous opportunities but also challenges for energy forecasting and optimisation.
With smart meter data, we are able to understand the energy consumption patterns better, with potential applications in smart homes, community energy management, robust grid operations, etc. On the other hand, smart meter data are distributed, big and essentially difficult to handle, making existing machine learning models intractable.
This project aims to develop a distributed and real-time machine learning framework for smart meter big data, and explore its applications including energy demand management and grid operations.
The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.
The successful applicant will have a relevant MSc degree with good numerical and computational background.
The candidate should also have good data analytics, mathematical, and programming skills with strong willingness and motivation to learn and develop.
Candidates with knowledge of smart energy systems and experience of working on machine learning and big data projects are particularly welcome to apply. Good programming skills in Java/ C++/ C are an advantage.
A minimum IELTS score of 6.5 (overall) is required.
You can apply for this postgraduate research opportunity online.
Please upload your CV, personal statement, transcripts of any undergraduate or Masters programmes and one reference.
Instruction to applicants
When you apply online you will be prompted to fill out several boxes in the form:
If you have any informal queries about this opportunity please email the lead supervisor, Dr Fanlin Meng (email@example.com).