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Field
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learning models keep getting better, but how they make their decisions often remains unclear, because these depend on many incomprehensible model parameters that have been learned from data. For instance
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phase equilibrium modelling facilitate the prediction of metal budgets of crustal magmatic systems (e.g., Weller et al., 2024 JPet, Soderman et al., 2025 Nat Geosci). Pseudosections, the bespoke phase
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analytical facilities hosted at UU’s Electron Microscopy Centre. Project description Recent advances in thermodynamic phase equilibrium modelling facilitate the prediction of metal budgets of crustal magmatic
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Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working at Utrecht University . Selection process As Utrecht
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responsibilities will include: Design and develop analog circuits for implementing ONNs for computing. Modeling, simulate and benchmark different computing tasks such as sensor data processing. Explore ONN
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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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maintain robustness through evolution using live-cell imaging and multiscale modelling. Job description Cells are often described as intricate machines where proteins work together in a tightly coordinated
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a given discrete-event model that is safe with respect to certain requirements. Yet, the application of the approach is limited by the fact that tools for synthesis cannot always deal with the state
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dialogue modelling), the series of experiments will result in four journal papers, to be integrated into a dissertation. You will participate in relevant conferences, workshops and seminars, and communicate
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mapping, with molecular approaches using human and animal disease models. Where to apply Website https://www.academictransfer.com/en/jobs/355039/phd-in-cardiac-electrophysiolog… Requirements Specific