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Field
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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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with numerical modelling and analysis and handling of large simulation datasets Strong knowledge of HPC programming (Fortran and C/C++) and performance optimization Knowledge of parallel programming
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 2 months ago
models). Your tasks Development of analytical theories of biomolecular phase transitions, focusing on liquid-liquid phase separation. Development of numerical simulation tools, e.g., for process modeling
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of selected heat integration or thermal energy storage concepts Modelling and preliminary sizing of heat exchangers and thermal energy storage components Simulation and comparison of selected system layouts
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Model. In parallel, the group is engaged in hardware activities related to the development and implementation of new FPGA-based trigger processors, both for the operation of the ATLAS muon spectrometer
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Postdoc (f/m/d) Methods Development for the Micromechanical Characterization and Modeling of the ...
., finite element simulation, crystal plasticity modeling, analytical/semi-empirical hardening models, or data-driven analysis methods, along with a demonstrated ability to independently develop new
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of the mission when LiDAR localization was reliable Semantic image features derived from foundation models, where applicable, to ensure robust (context-aware) recognition of these visual features The robot's
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
-/nanoindentation and complementary imaging techniques (e.g. AFM) # Hands-on experience in the numerical modeling of mechanical material properties, e.g., finite element simulation, crystal plasticity modeling
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. Finally, model outputs will be visualized and analyzed to identify and understand discrepancies between simulations and experimental observations. The number of model components to be calibrated (root
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. Evolve existing in-building fiber and wireless technologies to achieve adequate performance at a reasonable cost. Set up a simulation framework based on experimentally validated models. Conduct systematic