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of electrolysis process in green hydrogen production can be improved therefore ultimately lower LCoH, contributing to net zero by generating zero carbon emissions and reducing reliance on fossil fuels. Cranfield is
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the two triage methods. Yorkshire Ambulance Service NHS Trust is in the process of migrating from AMPDS to NHS Pathways, which presents a unique opportunity to determine which tool is the most appropriate
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techniques and advanced sampling methods to bring a significant advancement in reducing high-fidelity runs to accelerate the engineering design, validation process and improve the robustness of the prediction
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access to an NWSSDTP Research Training Support Grant for eligible research expenses. Application process Applications for this ESRC CASE PhD Studentship should be sent by email to the School of Law and
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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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aerodynamics will continue to be an important aspect including the improvement of fundamental understanding of complex flow physics as well as advancing aerodynamic methods for industrial design. The overall
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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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in materials with nanometre spatial resolution and up-to attosecond time resolution. These XFELs are especially well suited to studying process like ion hops which govern the charging rates battery
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biomedical engineering). Flexible start date! What You’ll Need A first-class or upper second-class honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics. Experience in coding and CFD
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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