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Field
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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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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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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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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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developing experimental therapies such as phage therapy. The candidate should have a good degree in a physical science and an interest the development of new diagnostic systems. If you wish to discuss any
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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
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itself and changes the way it should appear at high photon energies. The details of this process can be explored both analytically and numerically, the latter using simulations of magnetohydrodynamics (MHD