27 modeling-and-simulation-post-doc PhD positions at Eindhoven University of Technology (TU/e)
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this vacancy contributes to the development of the above EmPowerED toolbox by developing simulation models for individual physical components of a PED to be used as building blocks for integrated PED models
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roadmap for metrology . The project will involve mathematical modeling, numerical simulations and, potentially, measurements. You will mainly do your programming work in a mixed programming environment, i.e
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computational model to capture the complex transport of gases, liquids, and charges in these porous structures, including the complex interfaces between them. Insights from the model will directly guide the
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analog circuits for implementing ONNs for computing. Modeling, simulate and benchmark different computing tasks such as sensor data processing. Explore ONN implementation topology and its energy efficiency
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for implementing ONNs. Modeling, simulate and benchmark different computing tasks such as combinatorial optimisation tasks and solving partial/ordinary differential equations with ONNs. Design and tapeout ONN chips
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complemented with computational simulations of mechanobiology-mediated angiogenesis, to further dissect the contributions of cell signaling and environmental mechanical properties. The research will be conducted
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transitions, flow dynamics, stratification) inside thermal storages containing PCMs? How can we describe via validated multi-physics simulation models; heat transfer, flow behavior, and phase changes? Your task
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events, will be used to enhance the diagnostic and predictive capabilities of the safety level for the individual lift. Combining data-driven probabilistic risk assessment models and inspection planning
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, hardware prototyping, compiler design, simulation and emulation tools, as well as cybersecurity, reliability, and system verifiability. The objective of this PhD project is to develop a gain-cell memory
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sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and