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offers a unique opportunity to work at the intersection of computational physics, neuroscience, and biomechanics, developing physics-based simulations that bridge neural circuits, muscle activity, and
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on the second part of the project and will consist in using heterogeneous catalysts developed by the first PhD student in the oxidation of cellulose to determine the best catalysts formulations, along with
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Francophone literature, approached through methodologies developed in ecopoetics. Particular attention will be given to the contrasting literary models of utopia and dystopia, articulated in both realist and
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-source CPU-GPU coordinate-agnostic particle-in-cell (PIC) code ENTITY, the aim of the doctoral thesis is to characterize the dynamics of these shock waves, their long-term evolution, and their
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | about 1 month ago
could be key to the development of new therapeutic opportunities. To explore the relationship between intrinsic and extrinsic WM features, our team proposes an original PhD program involving the analysis
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. This approach will enable the development of a novel and versatile photonic platform that couples atomic systems with metamaterials. This work constitutes one of the central objectives of the ANR PCR project
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 21 days ago
been developed in the laboratory. It relies in particular on atomic force microscopy (AFM) operated in force spectroscopy mode and enables (i) access to local properties of mineral surfaces (topography
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allow for the refinement of the spatio-temporal monitoring of hydrological phenomena and support the development of more responsive and adapted management strategies. Risk management is thus part of a
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the interior of wildfire plumes. This technique could improve our understanding of pyroCb, and potentially provide real-time warnings to ground-based operations to anticipate the development of extreme wildfires
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from limited data; development of automated and interactive pipelines. • AI and image processing for anomaly detection: data acquisition (dataset creation and validation), segmentation of realistic data