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and with the 2AT team at Institut Pprime to develop a shape-optimisation tool based on resolvent analysis, applied to landing-gear aeroacoustics The researcher will develop a numerical methodology based
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evaluation of modal-decomposition techniques applied to data from high-fidelity numerical simulations of landing-gear aeroacoustics. The researcher will develop and implement modal-decomposition methods using
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 1 month ago
Job description:Postdoctoral Researcher (f/m/d) in Machine Learning and Surrogate Modeling for Geochemical Systems With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500
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Digital Twins: You will use numerical tools, including COMSOL and the open-source HemoCell simulation package, to model cell-scale thrombus formation. Experimental Validation: You will not just code; you
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fluid dynamics and/or magnetohydrodynamics, in addition to excellent numerical skills. The post-holder will also play a key role in the dissemination of the results from this project, so excellent verbal
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numerical analysis, computational fluid dynamics, and uncertainty quantification with diverse applications. Our group maintains active collaborations with other divisions at Linköping University and broader
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and with the 2AT team at Institut Pprime to develop resolvent-based modelling tools for turbulent jets in cases where the jet mean flow is three-dimensional. The researcher shall: - Develop a high-order
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University’s efforts to address the above challenge by supporting the development of advanced tools and models for the design and operation of active low-carbon communities and PEDs-rich LV distribution networks
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numerical modeling (ocean, sea ice, atmosphere) or in physical model emulation; Knowledge of data assimilation methods (4DVar, EnKF, En4DVar) and their practical implementation; Research experience in machine