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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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description of steroid enzyme mediated synthesis in the cortex of a healthy adrenal gland and implementing them into numerical simulation codes for handling complicated spatial profiles. Validating the models
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for all. This PhD project aims to develop a robust numerical modelling framework to improve understanding and prediction of heat and fluid flow in deep geothermal reservoirs. Using geological, hydrological
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of the vibrational energy. This project is intended to perform numerical analysis and modelling aimed at the optimisation and development of effective friction dampers. The research studies on friction damping will be
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fields: nonlinear PDEs, functional analysis, numerical analysis, PDE-based modeling and numerical simulations excellent command of the English language ability to teach in German and English We offer
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background in (not necessarily all) the following fields: nonlinear PDEs, functional analysis, numerical analysis, PDE-based modeling and numerical simulations excellent command of the English language ability
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integral methods, stochastic modelling, optimisation algorithms, or numerical simulation. Candidates with interest in electromagnetic theory, quantum mathematics, wave chaos and complexity, as
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necessarily require formal education in geotechnics. Applicants with a background in mechanical/materials engineering or alternatively mathematics/computer science with an interest in numerical modelling
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few. In this context, several numerical techniques have been proposed to track the continuous evolution of the binary fluids from an initially immiscible state to the miscible state[1] ,[2] ,[3
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material, and produces high-quality documents. Furthermore, you have a solid understanding of numerical data and can solve numerical tasks quickly and easily. Experience in route optimization and strong