Ceres is a global leader in solid oxide technology, driving the clean energy revolution. Our steel‑supported fuel cell and electrolyser technology provides partners with a decade of development advantage, enabling faster innovation across power generation, green hydrogen, and distributed power solutions for data centres and industry. At Ceres, innovation is part of who we are. Join us and become part of a world‑class team making a meaningful and positive impact on the planet.

Purpose of the role

The role sits within the department of Modelling and Digitalisation, a team of highly skilled modelling engineers, data scientists, and data engineers. The department specialises in multi-domain computational modelling, specialist data analysis, and the development of bespoke data products and cloud data platform solutions.

The role is focused on developing or adapting reduced order models of Ceres technology and then applying these to accelerate product and technology development, support existing and new partners, and explore new business opportunities. Specifically, you will be regularly exploring different applications, concepts, and ideas and assessing viability, from both the engineering performance and techno-economic perspectives. You will collaborate with a wider cross-disciplinary teams to maximise modelling value across the business.

Key responsibilities include delivering to different reduced order modelling work packages, collaboration across internal and customer-facing projects to solve challenging problems and support for intellectual property creation. You will also be responsible for ensuring that your models and insights are utilised across our full chain of modelling and digital tools within the department and the wider company.

Knowledge and skills required for the role:

• Degree qualified in a STEM discipline, or comprehensive experience demonstrating the below requirements

• Experienced in extracting insight from complex data using tools such as Matlab or Python

• Experienced in reduced order modelling of engineering systems and developing first-principles models of system components (core areas of interest are heat transfer, fluid dynamics, electrochemistry, and techno-economic analysis)

• Basic understanding of machine learning and surrogate modelling techniques to accelerate modelling workflows

• Basic understanding of analytical design and verification methods, and limitations of computational models

• Experienced in clearly communicating complex concepts to audiences at any level

• Above all, you have a proactive attitude, you love solving complex problems, and you’re a fast learner of new domain knowledge