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exciton transport to the reaction center or to a donor-acceptor interface, respectively. Characterizing, modeling and improving exciton transport properties in molecular materials remain essential tasks in
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network contracts upon local stimuli to orient fluid transport where it is needed. This enables autonomous functionalities without the need for a complex nervous system. These organisms use fluid transfer
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-symmetric complex. This structural symmetry ensures exceptional stability and performance under extreme conditions of temperature, pressure, and magnetic field. In our recent work, we have successfully
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and optimize specific functions. The ICMCB carries out fundamental research on model materials and/or materials with potential applications. The ICMCB is a UMR with an average of 280 agents (permanent
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specializes in computer science and, more broadly, in information and communication sciences and technologies (ICST). Its research activities are organized around several major axes: the modeling and
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since its discovery 150 years ago, due to a lack of model species 2,3. The candidate will use a combination of genetic tools to identify the molecular machinery responsible for chromosome fragmentation in
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software and models will be developed for data processing and analysis. Finally, the instrument capabilities will be evaluated using protocols and reference samples previously developed for this purpose in
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observations of the solar atmosphere, in-situ measurements from heliospheric probes, in synergy with a complete numerical modeling of the generation and propagation of solar jets from the solar atmosphere
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complementary expertise, using both space and ground-based observations of the solar atmosphere, in-situ measurements from heliospheric probes, in synergy with a complete numerical modeling of the generation and
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 18 days ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially