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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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of wiretap coding in highly directive links and to obtain new bounds for the finite-blocklength secrecy rate under a mutual information secrecy constraint. Context : Physical layer security techniques
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opportunities for machine learning to address outstanding biological questions. The PhD (M/F), to be recruited in the context of the ERC StG MULTI-viewCELL, will be working on the development of a new method
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quantitative solution of the elements of interest to the project (in particular Rh). At this stage, all activation methods are envisaged (chemical, microwave, hydrothermal, ultrasonic, etc.). Methods
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to the polymers, and complexing units to allow the polymers to interact with metal ions [1]. The target polymers will be synthesized by controlled radical polymerization or telomerization. The proposed method is
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to populations and infrastructure. While heavy rainfall is well recognized as a triggering factor, recent studies have revealed the importance of snow cover and, in particular, rapid melting dynamics in
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bodies moving in a fluid or fluids being transported in ducts and pipes. There is significant pressure to reduce transport-related emissions, of which friction drag is a major constituent. On the other
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-individual differences through a comparative approach in order to identify key, fundamental elements that are conserved or converge during evolution. To this end, we specifically intend to study the strategies
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different methods of analysis used in the community, in particular linguistic probes (classifiers trained to predict certain linguistic properties from representations discovered by neural networks
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of natural language texts. This PhD position aims at investigating new computational methods to reduce distortions in science communication. The envisioned methodology will focus on Argument Mining with a