13 modeling-and-simulation PhD positions at Technical University of Denmark in Denmark
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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tasks will be to: Develop and implement machine learning models for dynamic simulations of renewable power systems Develop comprehensive guidelines for verifying and testing dynamic equivalents Integrate
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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cutting in the production facility. Establish a numerical model to simulate the glass cutting process. Design experimental measurements and assist in the integration of sensors in production. Acquire
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, satellite altimetry, ice flow maps and terminus positions and other relevant data to constrain numerical model to simulate 1900-present and future (present-2100) ice flow changes under different UN IPCC
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challenges and decision-making under uncertainty. Ability to translate conceptual models to their mathematical formulation and to test them with numerical and simulation experiments. Excellent communication in
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with regular waves. Extending the model to full two-way coupling, allowing feedback from flexible vegetation on wave-induced flow. Applying the fully-coupled model to simulate interactions under both
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. Experience in modeling biological processes. Experience in use of process simulation tools such as Matlab, Aquasim, Superpro Designer or similar. Expertise within fermentation technology and/or gas
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failure analysis using advanced finite element models and simulation techniques. This is enabled by digital and sensor technologies such as artificial intelligence, computer vision, drones, and robotics
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key