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inverse problems, as well as the development of numerical methods that can eventually be applied to large-scale wave-imaging problems. The PhD project will have a strong mathematical component. You will
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methods that extend current sets of flood scenarios derived from physical and numerical models, incorporate climate change effects, and then use these enriched datasets to assess the future insurability of
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methods, image processing, high-speed imaging, cryogenic testing, composites, numerical modelling or non-destructive testing is an advantage. You can work independently while collaborating effectively with
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., plasticity, damage, fracture) in engineering materials at different length scales, which emerges from the physics and mechanics of the underlying multi-phase microstructure. An integrated numerical
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modeling, construction of numerical methods, coding, testing, numerical simulations, and possibly measurements. You will mainly do your programming work in a mixed programming environment, i.e. combining
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will be combined with theoretical modelling, statistical data analysis, and numerical simulations to identify the governing mechanisms and to determine whether and how proximity to a phase transition
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, Physics, Computational Science, or a closely related field. A strong background in numerical methods, scientific computing, computational physics, or quantum computing. Excellent programming skills in
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will work with researchers in environmental geo-spatial AI method development, soil modelling and spatial statistics. Where to apply Website https://www.academictransfer.com/en/jobs/362722/phd-position
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to increasing traffic loads and environmental effects, efficient methods for monitoring their condition become essential. Traditional inspection approaches can be costly, time-consuming, and limited in spatial
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traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict deterioration. This PhD project addresses this challenge by developing a novel