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PhD thesis student (f/m) at ESRF and University of Bath to study CO₂ capture by coupling X-ray Raman
critical electronic changes governing CO₂ capture under realistic conditions, providing the essential scientific insights needed to design more efficient, durable, and optimized materials for fighting
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and supramolecular chemistry, physical and analytical chemistry, materials design, and photonics. Five teams are affiliated with the CNRS National Institute of Chemistry (INC), while the sixth is part
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. This PhD project in Natural Language Processing aims to design generative models capable of automatically simplifying texts into Easy-to-Read and Understand language while preserving their discourse
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
Will Gain from This PhD This PhD offers the opportunity to: Develop highly sought-after skills in knowledge engineering, semantics alignment, and collaborative innovation. Collaborate with leading
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: • To design and optimize an electrolyzer that simultaneously promotes mass transfer, contaminant degradation, and gas recovery; • Study the influence of operating parameters (current density, reactor geometry
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perfusion and mechanical stimulation in tumor tissues to investigate their poromechanical properties and optimize molecular transport within explants. The successful candidate will be involved in: - Designing
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understanding of microstructure-property relationships in materials systems in the presence of different stimuli close to operational conditions. Such knowledge is crucial for designing advanced materials with
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optimization approaches • the comparison centralized, decentralized optimization techniques and complementarity-constrained formulations while for non-convex problems • the design and assessment of flexibility
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remaining biologically interpretable? The PhD candidate will design and apply integrative computational workflows using methods such as multi-omics integration, spatial modelling, representation learning
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) designs and/or studies materials with novel properties. This requires expertise in material synthesis, advanced characterization, physical property studies, as well as modeling and theoretical description