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value-added materials. Objectives of the PhD The PhD candidate will focus on designing and optimizing green spinning processes for lignin-based fibers, relying exclusively on enzymes and water, avoiding
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. The team's main research areas are: Architectures for Autonomous Robots, Learning, Temporal Planning and Execution Control, and Algorithmic motion planning. RIS is composed of 8 permanent researchers, 4
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD student will join a stimulating, collaborative
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on the University of Toulouse science campus, easily accessible by public transport, car, or bicycle. The CBI offers research teams an excellent international scientific and technological environment with innovative
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Description The PhD will take place at the Laboratory of Biotechnology and Cell Signaling (BSC – UMR 7242), located at the École Supérieure de Biotechnologie de Strasbourg (ESBS) in Illkirch. The doctoral
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networks, ensemble algorithms, and other advanced architectures, the objective will be to accurately predict the state of health (SoH) of batteries in the short, medium, and long term, including under
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 13 days ago
Emeraude INRIA/INSA-Lyon research team which is physically based at the CITI Lab of INSA-Lyon (Villeurbanne, France). This PhD will be conducted under the supervision of Romain Michon (Inria) and Pierre
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available for onsite translation, how the axonal and dendritic RNA landscapes are locally organized and regulated to integrate external local signals is to date unclear. The objective of the PhD project will
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. The successful candidate will work at the IGBMC and benefit from an exceptional international scientific environment. The IGBMC has around 40 teams divided into four scientific departments and state-of-the-art
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into **influence functions**, theoretical tools designed to quantify the impact of a sample on a machine learning model. These functions, defined through the derivative of model parameters or the loss function with