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Field
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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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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
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with a background in mechanical, aeronautical, automotive, civil / industrial and/or software engineering (or similar) and/or mathematics and/or physics. The ideal candidate will have a solid background
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with a first class or upper second-class degree in engineering, physics, applied mathematics or a related field. A solid foundation in fluid dynamics and heat transfer, and experience with computer
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of these complex designs will need advanced measurements from early development to flight testing. Optical sensing can provide high-resolution data to understand the underlying physics, potentially reducing