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complementary data from other Mars missions to strengthen current models and provide comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run
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and interaction between devices have a significant impact. In this project you will work on and develop numerical modelling capabilities for arrays of turbines with non-homogeneous flows and wake
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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Microscopy Center. The project further benefits from excellent dedicated CPU and GPU computing infrastructure to support large-scale numerical modelling and data analysis. This is a full-time, two-year
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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subsequent PhD degree in (astro-)physics, computer science, engineering, or other related fields, with a strong focus on numerical simulations Experience in the development of numerical methods for multi
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involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation. Your Profile: A Masters degree with a strong academic background in physics
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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invasions along environmental gradients and their consequences for community- and ecosystem-level responses, using native and invasive freshwater snails as a model system. The project builds on our previous
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activities regarding numerical modeling of tsunamis, both inundation, with a view to updating the national hazard model, and offshore propagation, with a view to using the new pressure sensors installed in