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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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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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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
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. Activities - Literature review and data compiling - Planification and realization of biochemical experiments (assembly of biochemical reaction networks) - Data analysis and regular (weekly) reporting
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on neural networks whose parameters are trained on simulations (SBI — Simulation-Based Inference). The recruited person will work within the ZTF, LSST/DESC and Lazuli collaborations to implement cosmological
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socio-hydrosystems, from stormwater networks and surface waters to wastewater treatment plants and receiving environments. The project relies on innovative high-resolution mass spectrometry (HRMS)-based
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Vector Network Analyzer (VNA) measurements. • Good knowledge of dielectric materials and their frequency-dependent properties. • Experience with vacuum instrumentation or controlled environmental testing