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# Senior Systems Modelling Engineer

- **Company:** Red Bull
- **Location:** Milton Keynes, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Computer Programming, Performance Tuning, Systems Architecture, Model Validation, Modeling and Simulation
- **Published:** May 30, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=967ebc7afda363bc

## About the Role

Do you have experience in Communication skills?, * Degree in Engineering, Physics, or a related field (postgraduate qualification advantageous).

 * Proven experience in thermo-fluid system modelling with strong fundamentals in thermodynamics, fluid mechanics, and heat transfer.
 * Proven ability to develop physics-based models using Modelica/Dymola or similar frameworks.
 * Solid understanding of numerical methods used in system simulation.
 * Strong analytical, problem-solving, and communication skills.
 * Desirable: programming experience, motorsport/automotive background, and experience with model validation, integration, and simulation toolchains.

## Description

We're looking for a Senior Systems Modelling Engineer to join our Vehicle Performance team, focusing on developing our thermo-fluid modelling capabilities. Initially centred on cooling systems, this role will expand into broader hydraulic and thermo-fluid applications.

Working closely with Chassis Design, Aerodynamics, Vehicle Performance, and Red Bull Ford Powertrains, you will develop models and tools that drive improved vehicle design and overall performance.

We're looking for someone who can:

 * Develop and maintain robust, physics-based thermo-fluid models (initially focused on cooling systems) within a Modelica/Dymola environment.
 * Build and deliver simulation tools to support system design and performance optimisation.
 * Contribute to the modelling and evaluation of future vehicle concepts and system architectures.
 * Collaborate closely with stakeholders to ensure models meet development and integration needs.
 * Perform system identification and validation to align models with test and track data.
 * Drive continuous improvement of modelling methods, tools, and workflows.


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