Research Assistant Or Research Fellow In Machine Learning For Energy Flexibility

Fixed Term Contract for 14 months or until 31 March 2025 (whichever is sooner) Research Assistant (if close to completing PhD): £34,450 per annum Research Fellow (if PhD obtained): £37,337 per annum Location: Cranfield, Bedfordshire We welcome applications from passionate and skilled researchers to join our team to contribute to developing Machine Learning frameworks for flexibility service provisions in energy systems. 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. Please do not hesitate to contact us for further details on E: hrrecruitment@cranfield.ac.uk. Please quote reference number 4716. Closing date for receipt of applications: 31 January 2024