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windows as well as to optimize the tooling geometry. These process simulations require efficient numerical algorithms to be practical and to enable robust optimization. Therefore, in this project you will
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, but current methods are not always efficient or optimal. The process lacks an intelligent, informed approach to selecting the best grinding parameters, which can lead to inefficient maintenance actions
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, ranging from soft matter to biology to synthetic chemistry. The laboratory benefits from numerous academic and industrial partnerships in France and worldwide. Its openness, recognition, and state
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nano-scale objects Designing and building a coherent low-energy electron microscope Sample preparation and recording holograms Numerical reconstruction of the sample structure Presenting the results
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-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from the microscale up
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Master's degree in cybernetics, control systems, or equivalent, with a strong training in numerical optimization and optimal control. Additional training in machine learning and reinforcement learning is a
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require efficient numerical algorithms to be practical and to enable robust optimization. Therefore, in this project you will: Develop efficient numerical methods and strategies to solve the electromagnetic
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systems, or equivalent, with a strong training in numerical optimization and optimal control. Additional training in machine learning and reinforcement learning is a plus. Your course of study must
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than conventional structures. Superstructure optimization, which uses a predefined architecture with numerous unit operations and possible paths, offers an alternative for identifying non-intuitive
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mechanical motion into electricity; - An intelligent electronic control system, enabling optimized regulation of the thermodynamic cycle. Within the CALIFORCE3 framework, the REMLA system will be integrated