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in reservoir engineering, geosciences, geotechnical engineering, or related fields. Experience in numerical modeling of flow in porous media and programming skills is highly desirable. Supervisors: Ana
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characterization techniques. The second phase will be dedicated to the development of a Multiphysics numerical model, validated and informed by experimental data, to simulate the WAAM process. Strong
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 6 days ago
) at the IT4Innovations, at the Material Design group of Dr. Dominik Legut within the Modeling of Nanotechnology Lab, is open to applicants for studying MATERIAL PROPERTIES OF MAGNETIC MATERIALS by means of the spin
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phases [1]. The main objective is to study these effects, never observed before in an experimental setting, through numerical modelling and laboratory experiments. This PhD thesis is part of a
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initiation and evolution. Different rheological parameterizations of yield-strength and dislocation creep will be compared in regional and large-scale numerical models of mantle dynamics. We will in particular
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tomography, computational resources, and laboratory facilities for experimental mechanics? The Division of Solid Mechanics conducts research within constitutive modelling, nonlinear numerical methods
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and numerical models as well as constitutive model calibration and validation based on physical experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 20 days ago
Modelling. Your tasks # Development and implementation of numerical and algorithmic methods for the simulation of fluid-dynamical or environmental systems # Research on quantum and hybrid quantum–classical
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Student (f/m/d) in Quantum Algorithms for Droplet and Bubble Oscillation Dynamics Modelling. Your tasks Development and implementation of numerical and algorithmic methods for the simulation of fluid
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model