Showcasing Impact at the Computational Fluid Dynamics TeamArup's purpose, shared values, and collaborative approach have set us apart for 80 years, guiding how we shape a better world.We have been present in Spain since 1993 and we have a team of more than 300 engineers, consultants, and qualified technical personnel. We work for clients from the public and private sectors, providing creative and sustainable solutions based on the efficient use of resources.Join a team that’s making a tangible difference. Our work in computational fluid dynamics is directly contributing to the design and safety of transportation infrastructure and complex buildings, supporting performance-based fire engineering and smoke management strategies through advanced numerical simulations.The OpportunityThis is your chance to join a team of specialists delivering cutting-edge international projects in the field of fire engineering and CFD. You will lead complex CFD studies related to smoke dispersion for performance-based fire engineering applications,



with special focus on ventilation of tunnels and metro/railway stations.As a senior member of the team, you will lead the delivery of complex CFD projects from proposal through to final reporting, mentor junior engineers, coordinate with specialists from other Arup offices worldwide, and contribute to the continuous improvement of modelling methodologies, workflows, and automation tools. You will also have the opportunity to participate in internal research and development initiatives (Invest in Arup), driving innovation in CFD modelling and contributing to the continuous advancement of our technical capabilities, simulation accuracy, and best-practice methodologies across the general fire engineering community.As a CFD Consultant at Arup, you will:Get involved in CFD simulations for a wide variety of projects, carrying out activities such as:Lead CFD studies for smoke movement and smoke control system design using FDS.Support performance-base

📌 Senior CFD Modeller (Madrid)
🏢 Arup
📍 Madrid