Research Assistant or Research Fellow in Machine Learning for Energy Flexibility

School/Department: School of Water, Energy and Environment
Based at: Cranfield Campus, Cranfield, Bedfordshire
Hours of work: 37 hours per week, normally worked Monday to Friday. Flexible working will be considered.
Contract type: Fixed term contract
Fixed Term Period: For 14 months or until 31 March 2025 (whichever is sooner)
Salary: Research Assistant (if close to completing PhD) £34,450 per annum Research Fellow (if PhD obtained) £37,337 per annum
Apply by: 31/01/2024

Role Description

About the Role

Energy system flexibility has the potential to essentially contribute to the decarbonisation and network constraints management, which has been widely harnessed, such as through the demand flexibility services (DFS). As part of a Department for Energy Security and Net Zero (DESNZ) funded project working with National Grid ESO and industry partners in energy modelling and digitisation, we aim to reinforce the DFS provisions with enhanced flexibility capacity and efficiency through a new machine learning-driven framework.

We are seeking a highly motivated individual with a strong research background in machine learning for energy systems. Your role will be to critically analyse the characteristics of load demand patterns associated with energy performance indicators, develop machine learning modules for clustering and classification, and predict flexibility for DFS provision. Working with our industry partners, you will undertake the evaluation of the economic and environmental benefits of an automated flexibility evaluation tool to stakeholders including National Grid, flexibility providers/aggregators, and consumers. You will validate the developed tool using case studies including domestic and community-level buildings, and accordingly evaluate the barriers and enablers.

This post will build on Cranfield University’s longstanding reputation in data science, electrical engineering, and energy systems management.

About You

You will be educated to doctoral level (or close to completion) in machine learning for energy systems or power engineering or related disciplines, and be able to demonstrate a sound understanding of energy systems flexibility. You will have strong analytical skills and an ability to work in a multidisciplinary team and engage confidently with industry partners.

You will have a track record of publishing high impact journal articles in machine learning or related aspects, and will have a commitment to scientific rigour, a passion for solving applied problems, and enjoy engaging with experts in academia and industry.

In return, you will have exciting opportunities for career development by collaborating with a vibrant, multi-disciplinary team, and to be at the forefront of world leading research and education, joining a supportive team and environment.

About Us

As a specialist postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here .

Cranfield Energy and Sustainability is one of eight themes at Cranfield University offering world-class and niche post-graduate level research, education, training and consultancy. Providing a sustainable, secure and affordable energy supply is fundamentally important to our lives. Cranfield is advancing the potential solutions in energy and sustainability to ensure our future needs are met.

As one of the four centres within Energy and Sustainability, Centre for Energy Systems and Strategy (CESS) pioneers multi-disciplinary approach by bringing together energy and power systems modelling, digital tools, social, policy and regulatory aspects of energy transitions, with a focus on simulation and modelling.

Our Values and Commitments

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here .

We aim to create and maintain a culture in which everyone can work and study together and realise their full potential. We are a Disability Confident Employer and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK by the charity Working Families . Find out more about our key commitments to Equality, Diversity and Inclusion and Flexible Working here .

Working Arrangements

Collaborating and connecting are integral to so much of what we do. Our Working Arrangements Framework provides many staff with the opportunity to flexibly combine on-site and remote working, where job roles allow, balancing the needs of our community of staff, students, clients and partners.                                 

For an informal discussion about this opportunity, please contact Dr Da Huo, Lecturer in Energy Systems Intelligence, on (T): 01234754779 or (E): [email protected]

Please do not hesitate to contact us for further details on E: [email protected] . Please quote reference number 4716.