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Field
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mechanisms/strategies, integrated with federated learning; i.e., scalable solutions to detect and block these attacks. In particular, this will involve moving towards the interpretability and transparency
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examples of high performance embedded systems. The carbon impact of such systems is currently dominated by the embodied emissions, i.e. the greenhouse gas emissions of system fabrication [G21], but results
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interactions of semiconductor nano-sensors with their surrounding bio-molecular environment under the action of intense electromagnetic fields (i.e. lasers) for molecular recognition in medical applications
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to an intermediate form of energy (i.e., magnetic, mechanical, radiative, electrostatic …), in the aim of wirelessly transferring this energy through a transmission medium (i.e., air, metal, water …). On the receiver
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matter often has hydrophobic or amphiphilic properties, i.e. it can be hydrophilic or hydrophobic depending on the water content. To take these physico-chemical properties into account, we define an energy
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involve rewriting new reconstruction algorithms so that they can be run on heterogeneous platforms, i.e. systems where a traditional processor (CPU) is coupled with an accelerator (e.g. a GPU). The PhD
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down to a nanoparticle enclosed onto a vacuum chambre, while being optically levitated (i.e. trapped) at the focal spot of a CW laser. The levitated nanoparticle will then describe mechanical
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) Accounting for drop size distribution variability across scales, and notably the one occurring below radar observation scales; (ii) Better understanding and quantifying the wind drift effect, i.e
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occurring below radar observation scales; (ii) Better understanding and quantifying the wind drift effect, i.e. the advection of raindrops are advected during their fall from radar observation elevations
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for the energy conversion i.e., the TE figure of merit of a material is defined by the dimensionless ZT = S2σ/(κe+κl) where S is the Seebeck coefficient, σ is the electrical conductivity T is the absolute