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in energy systems and tribology. You’ll collaborate with expert academics and fellow PhD students, fostering a dynamic and innovative learning environment. About John Crane Ltd As a world leader in
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), or international equivalent, in Engineering, Computer Science, Physics or Mathematics with evidence of programming experience. Equality, diversity and inclusion is fundamental to the success of RAINZ CDT and is at
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PhD Studentship available in the EPSRC Centre for Doctoral Training in Robotics and AI for Net Zero (RAINZ) This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
experts in the field, contributing to a dynamic, research-led environment. This project is sponsored by Rolls-Royce, a global leader in aerospace and defence innovation. The sponsor brings deep domain
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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the field of Modelling and Simulation (M&S), with a focus on biomechanics, biofluid dynamics, and machine learning applications in healthcare. Specifically, it addresses the simulation of cerebral blood flow
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materials. Over the last 40 years (Nobel Prizes in 1993 and 2024), scientists have developed exquisite laser-based tools for tracking material dynamics on these timescales. The problem is such methods (i
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) Physics, Chemistry, or Applied Mathematics Why This Matters The world is moving rapidly toward renewable energy. Hydrogen is at the forefront as a clean fuel, but its safe storage and use at high pressures
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simulating fluid networks and dynamic phenomena for assessing different solutions is a necessity The overall aim of this project is to improve the confidence in fuel system design process for ultra-efficient