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Field
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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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, 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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environmental footprint.In this context, the optimization of material recovery from end-of-life vehicle scrap is challenged not only by the heterogeneous and evolving nature of this scrap, but also by the diverse
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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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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
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reactor, with the goal of designing an optimized reactor for direct C2H4 production. Your colleagues: As a PhD Candidate, you will work in the plasmalab team in the Faculty of Science and Engineering
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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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of transverse transport on heat and mass transfer in a thermal arc plasma reactor, with the goal of designing an optimized reactor for direct C2H4 production. Your colleagues: As a PhD Candidate, you
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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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electrolysis and fuel cells (SOEC and SOFC). By combining numerical modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic