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Field
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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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as instructed. Purpose As a part of a multi-disciplinary, integrated research team, the candidate will participate directly in efforts to ground deformation assessment and numerical modeling of ground
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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
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numerical simulations using a GRMHD-PIC technique, which allows to track VHE ions on the fly. These simulations will be supplemented by cutting-edge analytical methods for injecting particles onto the grid
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sciences, particularly in the domain of numerical weather prediction. On the one hand, state-of-the-art models such as GraphCast have demonstrated outstanding predictive skill. On the other hand, physics
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problems, analyzing theoretical models of relativistic fermions in random potentials, advancing NPRG techniques for studying the interplay between interactions and disorder, implementing numerical
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analysis, AI algorithm modeling, testing, and integration into functional systems within the project scope. Specifically, in activities related to behavior modeling from IoT device data, generative AI
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cooperate with the PhD student at TU Dresden to link the modelling of glacial-isostatic adjustment (GIA) with the interpretation of geodetic GNSS measurements. You will focus your work on the numerical
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science, or a related discipline Experience in mathematical modelling and numerical methods for ordinary and partial differential equations Strong interest in working in a cross-disciplinary, collaborative
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security