17 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Empa in Switzerland
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of Computational Fluid Dynamics CFD environment and simulations including: - Computation of the microwave field, Coupling of the microwave field with the plasma - Computation of elementary ionization, recombination
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Fluid Dynamics CFD environment and simulations including: - Computation of the microwave field, Coupling of the microwave field with the plasma - Computation of elementary ionization, recombination and
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energy system models that incorporate a stronger Social Sciences and Humanities (SSH) perspective. By embedding societal dynamics, such models aim to capture a wider range of future uncertainties and
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plumes from point sources using the MicroHH atmospheric model. Analysis of plume dynamics and NOx chemistry in the high-resolution simulations. Develop and refine data-driven methods for emission
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the MicroHH atmospheric model. Analysis of plume dynamics and NOx chemistry in the high-resolution simulations. Develop and refine data-driven methods for emission quantification. Apply your methods to real
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that autonomously optimizes 3D velocimetry measurements by dynamically adjusting camera positions and optical parameters. Integrating the framework within a digital twin environment for pre-training and simulation
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activation and micromechanical modeling Progressive damage modeling of reinforced FRPs Mechanical characterization and fracture experiments Complete a PhD thesis at ETHZ Your profile Highly motivated and
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continuum modeling (finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and
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(finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real