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Field
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image data. The research explores how AI-driven analysis can move beyond manual reverse-engineering workflows by automating feature extraction and structural interpretation while remaining robust to noise
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it is scalable, has a wide aperture (to maintain a good signal-to-noise ratio), a wide bandwidth, and avoids mechanically movable components. Nonetheless, it must be able to capture light signals from
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nanogenerators. They enable self-diagnostics and vibration energy harvesting to make platforms more robust and energy efficient. Highly customized 3D-MIDs may be rapidly fabricated using multi-material extrusion
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for this position. Experience with experimental investigations of vibrating structures and with teaching in the field of engineering mechanics are advantageous. We are looking for a team player with a proactive and
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to noise, obfuscation, and architectural variability. Because the work operates at the intersection of scientific research and hardware security, the project carefully balances methodological innovation with
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responsivity and quantum efficiency, dark current, noise, linearity, uniformity and stability. You will also investigate detector integration with the overlying photonic-crystal filters, including alignment and
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governing vibration responses measured by LDV and to improve the understanding of how embankment condition influences train-induced dynamic behaviour. Transfer functions linking LDV, ABA, TG, and wayside
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characterised through the analysis of vibration responses generated during train passages. With every train passage, a comprehensive set of dynamic response data will be collected, creating a continuously
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dynamics (MBD) approaches. The model will be used to investigate the mechanisms governing vibration responses measured by LDV and to improve the understanding of how embankment condition influences train
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resulting structural responses. The health condition of railway tracks on bridges will then be characterised through the analysis of vibration responses generated during train passages. With every train