Description of the contracting Organisation
The contracting organisation is Trina Hydrogen - a technology company specialising in the development and commercialisation of solutions for the energy transition, with an international presence and experience in projects relating to renewable energy, energy storage, hydrogen and new sustainable fuels. As part of its R&D; strategy, the company aims to strengthen its capabilities in the digitalisation, modelling and advanced optimisation of energy facilities through the development of new in-house tools based on digital twins and artificial intelligence.

Objective of the R&D; project
To develop a modular and scalable digital twin platform for the design, simulation, optimisation and technical-economic analysis of renewable hydrogen facilities and their main derivatives. The platform will be centred on hydrogen production via electrolysis and will enable the gradual incorporation of hydrogen refuelling stations, synthetic sustainable aviation fuels, green ammonia and methanol, as well as their integration with renewable generation,



electricity storage and auxiliary systems.
The models will incorporate electrical operating constraints, conversion losses, equipment degradation and availability where relevant to sizing and operating decisions. Extensions of the platform will be prioritised within the three-year programme according to data availability, validation resources and technical relevance.
Academic profile
We are seeking a professional holding a PhD in one of the following areas or equivalent disciplines:
• Electrical Engineering.
• Control and automatization engineering.
• Computer Engineering with a specialisation in industry or energy.
• Applied mathematics or physics with experience in energy systems modelling.

A doctoral thesis or research experience relating to digital twins, energy system modelling, process simulation, optimisation, artificial intelligence, renewable energy, hydrogen or technologies for convertin

📌 Digital Twin (Madrid)
🏢 Trinasolar
📍 Madrid