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, annotators, and underlying neural architectures. The ultimate goal is to produce argument mining tools that are more robust, generalizable, and fair, applicable to sensitive domains such as the analysis
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Processes (https://simap.grenoble-inp.fr ) - is a multidisciplinary laboratory with more than 200 participants from chemistry, physics, materials and fluid mechanics. It is one of the leading laboratories in
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): the doctoral candidate will work to SAFRAN's site in Colombes (France). Objectives This PhD project aims to investigate the influence of ceramic shell microstructure and cluster architecture on the thermal shock
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architectures are expected to operate in diverse and critical environments such as hospitals, data centers, industrial sites, and isolated power grids. In this context, microgrids must: • Operate in grid
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for Systems Analysis and Architecture (LAAS) within the MechaBioFluidics team, which has extensive expertise in microfluidics and microfabrication (a technology platform of the national ReNaTech network), in
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tools for assessing material lifetime and performance, in connection with the data architectures developed within the REFFRACTEUR project. All modelling developments will be implemented in the open-source
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architecture that makes advanced agricultural robotics and data analytics accessible to non-expert users while supporting more precise, accountable, and environmentally responsible farming. Where to apply
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production. By developing hybrid architectures combining ontologies, generative models, reinforcement learning, and uncertainty quantification, the PhD project addresses the challenges identified by ICCARE in
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Analysis and Architecture (LAAS) within the MechaBioFluidics team, which has extensive expertise in microfluidics and microfabrication (a technology platform of the national ReNaTech network), both located
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches