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Field
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Institut Polytechnique de Paris, is one of France's top 5 general engineering schools. The mainspring of Télécom Paris is to train, imagine and undertake to design digital models, technologies and solutions
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deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery. Submit a
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numerical methods for the inverse design of nanophotonic structures enabling broadband, multiparameter sensing in fiber-based systems. The project is a collaboration between AMOLF, TU Eindhoven, and
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of conceptual and numerical models describing sediment material properties, sediment and water transport, leveraging in-situ and indirect measurements such as geophysical and/or remote sensing
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate
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in development and validation of multiscale numerical modeling techniques. Additional Application Materials Required: Special Instructions to the Applicants: Applications should include: (i) a cover
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computational fluid dynamics (CFD), cardiovascular modeling, or biomechanical growth and remodeling. Demonstrated experience with numerical methods (e.g., finite element method), programming languages (C
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of reproducing aerodynamic loads in real time for physical experiments. The project combines numerical modelling, data-driven methods, and laboratory experimentation to advance next-generation real-time hybrid
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. Numerical modeling of Mercury's magnetosphere and ion dynamics 2. MSA instrument data analysis LPP (Laboratory of Plasma Physics) is a joint research unit of the CNRS, École Polytechnique, and Sorbonne
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for candidates with a strong computer science background, such as algorithms, machine learning and data science. Key Responsibilities Develop, implement, and evaluate machine learning and deep learning models