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photonic systems, in particular, make it possible to harness the richness of optical dynamics to perform complex operations inspired by biological neural networks. However, current approaches face
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PhD position: Nanoengineering refractory compositionally complex alloys for extreme conditions (M/F)
science. He/she will join a top-tier network of academic and institutional collaborations across Europe and the United States. Finally, this PhD offers an outstanding research training experience, highly
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observed in optomechanical platforms [2]. Practically, the applicability of modern quantum technologies in optomechanical networks ultimately requires quantum entanglement of light and many vibrations—i.e
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emulsion stepwise polymerization and dynamic covalent networks to synthesize functional latexes with a regular and dense distribution of dynamic/cleavable thioester [-S-C(=O)-] or disulfide [-S-S-] clusters
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characterization resources whose capacities and performance are at the highest European level. The IEMN is part of the RENATECH network of major technology centers. Frequency-comb lasers are light sources that emit
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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energy materials—and is equipped with state-of-the-art research facilities. Embedded in a dynamic network of industrial and academic collaborations, SIMaP provides an ideal environment for ambitious
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The expert will participate in the necessary methodological developments and analyses of airborne data recorded by the IAGOS research infrastructure (https://www.iagos.org ) and from other networks, to provide
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• Participation in the international Long-Term Ecological Research network or a similar ecological research network • Experience working with spore-forming Firmicutes (bacilli) and/or Actinomycetes (culture
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning