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to cases with low signal-to-noise ratio and low-power operation. A first research direction will focus on investigating innovative energy harvesting techniques aimed at powering sensors without the use
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and ultra-stable Fabry-Perot cavities. This platform ensures the possibility to measure phase noise and frequency stabilities of optical signals in the 10-16 range and below. The team is a first-circle
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vibration training combined with transspinal direct current stimulation (tsDCS); conducting neurophysiological experiments under the supervision of the project manager; assisting in the organization
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the integration of advanced sensing technologies, digital twins, and Internet of Things (IoT) infrastructures. In acoustics, monitoring technologies have traditionally focused on environmental noise monitoring and
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: • Development of stochastic-dynamics inference methods: parametric and non-parametric approaches, latent-variable augmentation, treatment of colored noise (memory, subdiffusivity, temporal correlations). • Theory
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structural systems and ULS / SLS design concepts Experience in structural dynamics and/or vibration analysis is an added asset Familiarity with finite element modelling (e.g. ABAQUS) and probabilistic methods
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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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Doctoral Network funded by the Marie Sklodowska-Curie Actions (MSCA), dedicated to advancing innovative solutions for vibration and noise control in lightweight structures (https://cordis.europa.eu/project
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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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networks for quantum communication and networking. In contrast to classical optical signals, quantum states are extremely fragile and degrade rapidly due to loss, noise, and decoherence, especially over long