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the multi-scale multi-physics nature of the problem, solutions for a reactor relevant operational regime are hard to achieve given slow numerical convergence rates. An employment of fast numerical tools
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mission of unlocking the “geometry of artificial intelligence” then please apply! What you will do Singular Learning Theory (SLT) is a mathematical framework for analysing statistical models that do not
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contribute to a major research project to improve our understanding of macroplastic pollution in coastal seas. Working at the intersection of numerical modelling and observations, you will develop simulations
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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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, but also to develop approaches to prevent damage. In the project we aim to develop a numerical model to predict root growth. Your task as a Postdoc researcher is to develop a measurement and monitoring
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farms. This research will build upon a multi-scale and multi-physics numerical modeling framework, with the goal of enabling rapid and robust probabilistic assessments for design, optimization, and
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patterns. You will play a leading role in designing and carrying out numerical experiments, analysing large observational and model datasets, and translating process-level understanding from LES
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, metamaterials, and/or soil–structure interaction, and proven skills in experimental design and testing as well as numerical modelling (e.g., FEM). Experience with railway dynamics, soil mechanics, or nonlinear
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-structure interaction of the Maeslant Barrier, combining field modal testing, numerical modelling, and signal processing to capture and interpret its dynamic response under current and future hydraulic and
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. You will develop numerical models for nanoscale heat dissipation to interpret the experimental data. The project will be supervised by Prof. Zijlstra (Molecular Plasmonics group) and co-supervised by