Research Associate in Learning-based Predictive Control of Multi-Energy Systems

Overview

In this role you will develop, implement and test predictive control algorithms to coordinate multi-energy resources and grid infrastructure, enabling smarter operation of urban energy systems. You will apply learning-based methods to model dynamics, tune costs and constraints, and give control laws adaptive capabilities. You’ll collaborate with UK university researchers on multi-energy networks under the EPSRC Supergen Energy Network Hub, contributing to leadership and knowledge sharing in the energy systems community. The position involves UK travel and opportunities to present at international conferences, with a flexible working arrangement.

Pay / Benefits

• fantastic market leading Pension scheme
• health and wellbeing services including an Employee Assistance Programme
• exceptional starting annual leave entitlement, plus bank holidays
• additional paid closure over the Christmas period
• local and national discounts at major retailers

Responsibilities

• Develop, implement and test predictive control algorithms for multi-energy resource coordination
• Apply learning-based techniques to learn system dynamics and tune control parameters
• Collaborate with researchers across UK universities on modelling, optimisation and control
• Engage in dissemination activities including presenting at conferences
• Travel within the UK to collaborating institutions and interact with multidisciplinary teams
• Contribute to the EPSRC-funded Supergen Energy Network Hub project and related initiatives

Key requirements

• collaborative mindset
• effective cross-disciplinary communication
• ability to work with multiple stakeholders
• predictive control
• multi-energy networks modelling
• optimisation

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