Research Associate in Advanced Energy System Modelling

Grade UE07: £41,064.00 - £48,822.00 per annum

School of Engineering / College of Science & Engineering / Institute for Energy Systems

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 increasing 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.

Click to view a copy of the full job description (opens new browser tab).

As a valued member of our team you can expect:

• A competitive salary.
• An exciting, positive, creative, challenging and rewarding place to work.
• To be part of a diverse and vibrant international community.
• Comprehensive Staff Benefits, such as a generous holiday entitlement, competitive pension schemes, staff discounts, and family-friendly initiatives. Check out the full list on ourstaff benefits page (opens in a new tab) and use our reward calculator to discover the total value of your pay and benefits.

Championing equality, diversity and inclusion

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality.

Prior to any employment commencing with the University you will be required to evidence your right to work in the UK. Further information is available on ourright to work webpages (opens new browser tab).

The University may be able to sponsor the employment of international workers in this role. This will depend on a number of factors specific to the successful applicant.

Key dates to note

The closing date for applications is 12th September 2025.

Unless stated otherwise the closing time for applications is 11:59pm GMT. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone.