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Field
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network, including 4 training schools and two workshops. As a participant of the project, the PhD student will become part of a team at DTU with numerical and experimental expertise in photonic computing
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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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are as follows: Optimize and develop new measurement protocols for single-crystal paleointensity, in particular by combining this technique with the use of a "multispecimen" approach. Test a potential bias
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the initial phase, you will develop and optimize physical and numerical models describing the electron optics of the complete probe-forming column, including the multi-beam generation unit, imaging lenses
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three main phases. In the initial phase, you will develop and optimize physical and numerical models describing the electron optics of the complete probe-forming column, including the multi-beam
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geometry, even in cases in which the geometrical definition is not optimal, i.e. poorly defined or “dirty” geometries with no additional operations nor user intervention. Functions to be developed: Within
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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
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linkages based on numerical simulations and to transform them into AI- and ML-ready information to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit
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and designing sustainable P2X value chains. As a PhD researcher, you will contribute to the new stack designs for high-temperature electrolysis and fuel cells (SOEC and SOFC). By combining numerical