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characterization of the wireless link (power + sensing) - Design and optimization of sensor-resonator coupling - Participation in in vitro and ex vivo testing We are seeking highly motivated researchers to join our
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optimize hardware neural networks made of approximately one hundred magnetic tunnel junctions, with radio-frequency inputs, in order to classify RF signals directly in the physical domain. Chains of magnetic
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and fabricating transparent and flexible microelectrode arrays (MEAs) based on conductive polymers for recording the electrical activity of enteric neuronal networks, as well as for performing cellular
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characterization of microfabricated optomechanical devices used as gravity sensors. The postdoctoral researcher will also contribute to the scientific supervision of a PhD student working on these topics. Design
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models, whilst respecting the specific spatial structure induced by river networks. - Cleaning, structuring and analysing historical long-term monitoring data (approx. 200 sites). - Adaptation and
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insulator, localization, etc.) and to study their performance as thermal sensors for bolometers operating at low temperatures. The team thus covers the entire experimental cycle, from the design and
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needed for wet-bulb temperature retrieval -Co-locating satellite data with ground-based HadISD stations -Running neural networks and finding the architecture best suited to a case study on India
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to the dissemination of ultra-stable frequency and time references over telecommunication networks. Its optical-fiber network and laser stations also provide an experimental platform for the development of new
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the simulation of turbulent flows using a tensor network representation of the Navier–Stokes equations. Unlike recent approaches based on tensor networks, which simulate fluid flows in physical space using finite
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer