JED-AI Research Associate (782631)

Your research will focus on developing AI-driven recommendation tools that will be used in field studies whose aim is to improve our understanding on how engineering and socio-technological work can be brought together to ensure just and inclusive net zero transition.

FTE: 1 (35 hours/week)
Term: Fixed Term (Until 31/12/2026)

The Department of Electronic and Electrical Engineering (EEE) within the Faculty of Engineering is looking to fill a Research Associate post to work on a UKRI-funded JED-AI (Justice, Energy, Demand flexibility and AI) project.

JED-AI project brings together electrical engineering and social science to develop an inclusive methodology for learning about and strengthening households’ capabilities to participate in energy demand flexibility services through interdisciplinary co-designed household segmentation and interventions. The aim is to help realise a more just and sustainable net-zero transition. JED-AI will provide clear answers to the following research questions:

1. How do different household-and community- scale attributes and capabilities shape, enable and constrain household participation and engagement in energy demand flexibility?
2. How can AI analysis be combined with social science insights to generate more just and sustainable interventions in energy demand flexibility?
3. What are the key productivities, effects, challenges and benefits of interdisciplinary approaches to stimulating equity and justice in the net zero transition?

Through mixed methods analysis, JED-AI will explore how social science data and methodologies can enhance the trustworthiness of AI-based recommendations as well as how engineering-designed AI recommendations can provide an evidence-base to scale up social science intervention methods. The lessons learnt will have much wider implications in the way AI-informed energy demand research is designed and deployed for trials, taking advantage of the detail from social science processes to reduce risk of harmful decisions and removing human agency, and embedding inclusiveness and fairness.

To be considered for the role, you will hold a PhD in Computer Engineering, Electrical and Electronic Engineering or equivalent. You will have sufficient breadth and depth of knowledge in energy end-use, machine learning or signal processing with good programming experience (Python). You will have knowledge of appropriate research methods, have an ability to plan and prioritise your own workload, work independently, and you will have an ability to work within a team environment.

You will have excellent interpersonal and communication skills, with the ability to engage and persuade, and to present complex information in an accessible way to a range of audiences.

Formal interviews for this post will be held on Monday, 26 January 2026.

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