Research Associate in Advanced Energy System Modelling

Full-time: 35 hours per week

Fixed-term: 18 months

We are seeking a highly motivated postdoctoral researcher to join the University of Edinburgh’s Institute for Energy Systems, contributing to cutting-edge research on digitalised, low-inertia power networks. The role will focus on the development of open-source optimisation models for the GB electricity system with high levels of renewable generation, low system inertia, and increa sing offshore integration. The position is initially offered for a fixed term, subject to extension depending on project needs, funding availability, and performance.

The Opportunity:

The successful candidate will focus on developing advanced machine learning and mathematical optimisation frameworks for energy system modelling, particularly under the growing presence of offshore wind energy penetration. The successful candidate will lead the design and implementation of scalable energy system model that enhance grid flexibility, resilience, and economic efficiency.

In parallel, the role will investigate the coordinated management of emerging large and dynamic loads such as data centres, developing strategies to ensure secure and cost-effective integration within modern power networks. The research will incorporate realistic operational constraints, network dynamics, and policy-driven requirements.

The postholder will also benefit from opportunities to visit and collaborate with other world-leading universities and research institutions in the field, enabling further interdisciplinary engagement and international visibility.

This position sits at the interface between power system engineering, artificial intelligence, and mathematical optimisation, and will directly support innovation in the planning and operation of renewable electricity systems. This position is funded by UKRI, as a part of SIF Beta – Network DC Circuit Breakers project.

Your skills and attributes for success: 

• A PhD (or near completion) in Electrical Engineering, Control Systems, Data Science, or a related field.
• Proven experience in energy systems modelling, optimisation, or machine learning techniques.
• Publication record appropriate to career stage.
• Ability to work independently and as part of a team, including cross-disciplinary collaboration.
• Hands-on experience with open-source energy modelling frameworks.
• Excellent communication skills, both written and verbal.

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