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physics is especially interesting due to the presence of exotic excitations, potentially non-Abelian. The TensQHE project aims to develop modern numerical tools based on tensor networks, within an open
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uncertainties (delays, resources, failures) using various methods, including Bayesian approaches. 3. Optimize the workshop configuration, taking into account scenario variability, by relying on the surrogate
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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polariton accelerator” will exploit exciton–polaritons to implement artificial neural networks directly in hardware rather than in software. Exciton–polaritons are hybrid quasiparticles composed of light
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the European Research Council (ERC) Starting Grant, aims to understand the molecular evolution and adaptability of Deltaviruses to new hosts. The host laboratory benefits from a network of local, national, and
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. While processors and network interface cards can be time-multiplexed, RAM is spatially shared. Memory is therefore often the bottleneck when dimensioning and operating data centers. The candidate will
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over the course of the project. References: - Deneu B et al (2021) Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput
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systems. In particular, highly functional flexible electrochromic (EC) systems involving the integration of several active materials (metal oxides, electrolytes, network of metal nanowires used as
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that couples high‑performance computing with instrumental platforms integrated in the UPPA‑Tech network. Five platforms are directly supported by IPREM: POLYCaTS (polymer analysis and characterisation