Research Fellow in Offshore Digital Twins

The successful applicant will be joining a large, international consortium with dozens of academic and industrial members. You will look into many novel aspects of developing offshore digital twins, such as sensing, data handling, physics and AI modelling, software development, as well as experiments at the Cranfield Ocean Systems Laboratory. You will also be involved in assessing the techno-economic performance and life-cycle sustainability of the newly developed technologies. You will assist the coordinator in managing the project. You will have the opportunity to travel extensively to work with partners and attend international events.

You should have a PhD (or close to completion) in the area of Mechanical Engineering, Ocean Engineering, Electrical Engineering, Software Engineering, or related disciplines. You are expected to demonstrate knowledge and a track record in some of the following aspects: offshore wind turbines, wave tank experiments, fluid-structure interactions, sensors for offshore structural health monitoring, software development, machine learning and advanced AI modelling, and previous experience of working in large international collaborations.

Being independent and confident, having good planning and self-management skills, having great oral and written communication, and having a passion for teamwork will be ideal.

As a specialist postgraduate university, Cranfield's world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here.

Cranfield's Automotive, Energy and Photonics theme is an industry-focussed research cluster at the University and aims to bring together interdisciplinary teams to tackle existing and rising challenges for a Net-Zero future. Our researchers, academics and students engage in projects with industry and government to tackle issues ranging from offshore placement of renewable energy systems, to desktop-based development of advanced AI systems.

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