104 postdoc-finite-element-microstructure Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY in Morocco
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feasibility. Exploring novel material recovery methods to enhance metal extraction and reusability. Optimizing existing recycling processes to reduce energy consumption and environmental footprint. Assessing
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of novel cathode compositions, understanding degradation mechanisms, and improving electrochemical performance. The postdoc will work with state-of-the-art facilities for materials synthesis
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on the development of novel cathode compositions, understanding degradation mechanisms, and improving electrochemical performance. The postdoc will work with state-of-the-art facilities for materials synthesis
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electrochemical performance. The postdoc will work with state-of-the-art facilities for materials synthesis, structural and electrochemical characterization, and performance evaluation of Na-ion cathode materials
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electrochemical performance. The postdoc will work with state-of-the-art facilities for materials synthesis, structural and electrochemical characterization, and in-depth performance evaluation of Li-ion cathode
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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear
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will use finite volumes methods combined with physics-informed neural networks (PINNs) which offer a flexible technique that merges data-driven approaches with the underlying physics principles, enabling
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Job Description As an element of our scientific research activities at the CBS department, we are conducting innovative research on the development of advanced materials for biomass conversion
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Département / Department : Unité / Unit : Center for African Studies Université / University : Mohammed VI Polytechnic University Intitulé de l’offre Postdoc / Title of the Postdoc offer : African
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the coupled numerical model that we have developed for surface and subsurface flows. We will use finite volumes methods combined with physics-informed neural networks (PINNs) which offer a flexible technique