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in the SPG. We will make use of models of different complexity up to complex Earth System models, and modelling efforts for different past periods. A personalised training programme will be set up
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moving monopoles (aeroacoustic sources). Among other factors, such a source model can be parameterized by the vertical wind speed profile at the turbine. The propagation model will rely on numerically
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-cutting and bending to break the glass panels. The project will involve the establishment of a numerical model and the acquisition and analysis of data from physical measurements in the production
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shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
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with researchers from climate physics, hydrology, sustainability science and complex systems dynamics and apply a range of different models. Starting from the recent AMOC tipping simulations performed
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skills and experience with numerical modeling and particle-based methods Interest in working closely with experimentalists Excellent written and spoken English skills Experience with parallel programming
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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of recycled aluminium. More specifically, the project will focus on advanced numerical methods to understand how defects and different microstructures affect the strength of mega-cast components. As a PhD
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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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resource-constrained environments, and it is important to investigate whether features derived from different network layers can be effectively combined. Machine Learning Model Development & Optimization