Ceres is a global leader in solid oxide technology, driving the clean energy revolution. Our steel-supported fuel cell and electrolyser technology gives partners a decade of development advantage, accelerating innovation in power generation, green hydrogen, and distributed power solutions for data centre's. Trusted by some of the world’s most progressive companies, our proven designs deliver efficient, low-cost solutions backed by over 5 million test hours. At Ceres, innovation is in our DNA. Join us and be part of a world-class team making a 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 reduced order models for cells, stacks, and systems using physics-based models, as well as surrogate and machine learning methods. The aim is to accelerate product and technology development, support our existing and new partners, and explore new business opportunities. You'll collaborate with cross-disciplinary teams to maximize modelling value across the business.

Key responsibilities include leadership of 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 utilized 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, to a Bachelor, Master, or PhD level (e.g. Mechanical, Automotive, Aerospace, Chemical & Process Engineering)

• Several years of experience in modelling engineering systems with tools like Simscape or Modelica, focusing on heat transfer, fluid dynamics, or electrochemistry

• Experience in modelling in tools such as Comsol or Ansys is beneficial

• Experience in machine learning and surrogate modelling techniques to accelerate modelling workflows is beneficial

• Ability to extract insight from complex data using tools such as Matlab or Python

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

• Ability to clearly communicate complex concepts to audiences at any level

• You have a proactive attitude, love challenges, and you’re a fast learner of new domain knowledge