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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 1 month ago
, Arts et Métiers and CNAM, dedicated to innovation in the fields of mechanical engineering, materials science and advanced numerical simulation. Located in the heart of the 13th arrondissement of Paris
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with a surface. This project will involve using and further developing both the experimental and data analysis methods that are currently used within the research team. The student will learn how to use
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
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, foreseen in the application: 1-Development of the SUPERB framework 2- Definition of building classes and numerical models for physical vulnerability assessment 4-Definition of baseline data reflecting
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hospitals, allowing continuous monitoring of its progression. Current diagnostic methods have numerous limitations – long waiting times for results and low specificity. The aim of this project is to address
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acquisition and statistical methods is an advantage. Experience with numerical simulations (FEM). You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent
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phase-field modeling and/or FEniCS are a plus A keen interest in computational mechanics and in scientific methods and research in general We offer Your job with impact: Become part of ETH Zurich, which
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methods Knowledge of handling radioactive materials is considered an asset Experience in radiochemical methods is considered advantageous Highly qualified and highly motivated graduates and interested in
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performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators (e.g., Kalman Filter