42 computational-physics-simulation-"Prof"-"Prof" PhD positions at Cranfield University in United Kingdom
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves
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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
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to support condition-based predictive maintenance for gas turbine engines. Cranfield has developed unique physics-based technologies on gas turbine performance simulations, diagnostics, prognostics and lifing
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
at scale? Digital twins offer a promising foundation, but to truly support engineering decisions, they need to go beyond simulation and begin to interpret and reason about the systems they represent
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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physics background. Experience of experimental and computational modelling of icing physics, instrumentation and imaging techniques would be an advantage. Funding The Centre of Propulsion and Thermal
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in our CDT program, and warmly encourage applications from students of all backgrounds, including those from underrepresented groups. We particularly welcome students with disabilities, neurodiverse
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Rolls-Royce, this project will use both experimental and computational aspects to explore the aerodynamic design space for coupled intake/fan configurations that are required to deliver more efficient
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical