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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 1 month ago
process control requiring high-performance numerical tools for upstream optimization of the process PhD objectives: The CINDERELLA project aims to accelerate the mastery of the L-PBF process by considering
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solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast
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. The applications of the materials studied are numerous: building construction, transportation, energy, health, environment, fashion, and more. This work is part of the ANR TAFFTA project, "Towards high value-added
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: Applications accepted all year round Details Data assimilation combines physical models with experimental or numerical data to produce dynamically consistent flow reconstructions. In turbulence, where full
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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activity and stochasticity). For example, localized dendritic activation underlies numerous computational functions across hierarchical levels, such as denoising (filtering), increased expressivity (tunable
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. MINDnet aims at addressing the challenge through a holistic optimization - from individual computing devices to the overall architecture, including a focus on applications, and training methods - across
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Description The Industrial Informatics group at FEE/RICE is focused on the application of advanced optimization techniques to: i) design of new electro-mechanical devices using numerical simulations, ii) system
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take