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be located in Central Cambridge, Cambridgeshire, UK. The key responsibilities and duties are to conduct direct numerical simulations of turbulent flows over rough surfaces under non-equilibrium
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highly numerate individual with a strong interest in handling ocean data from diverse platforms to gain important new insights into this highly climatically-relevant part of the world’s ocean. We require
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to constrain the depth of the magmatic pressurization source (5). Training The candidate will gain skills in seismic data processing, tomographic imaging, and numerical modelling. Travel opportunities include
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team player with strong scientific interests, self-motivation, combining an aptitude for practical research with numerical skills. You will have a degree in natural sciences, environmental sciences
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, differential equations, geometry/topology, numerical analysis, optimization, and statistics. Part of the research is also carried out in close cooperation with other fields of science and technology at NTNU, as
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King's College London Department of Engineering | London, England | United Kingdom | about 1 month ago
arising from different vegetation fire types such as crown fires, shrub fires, and smouldering fires. A methodology to link lab-scale and field-scale fires. Numerical model of ignition with a database of
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sensing (e.g., PlanetScope, Sentinel-1), advanced numerical modelling (HEC-RAS, Delft-FM), and targeted field surveys to map mining intensity, simulate channel adjustment, and assess changing flood hazards
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Earthquake Engineering and examines the dynamic and liquefaction behavior of Icelandic basalt sand, using laboratory test methods and advanced numerical techniques. The project is funded for three years by
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critical role. The research will combine: Numerical modelling: develop and validate models to describe transport and separation mechanisms Experimental work: Design and operate and experimental setup using
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in a variety of lossy media. The project will give you an opportunity to work with theoretical, numerical and experimental aspects of machine learning assisted design of antennas and metasurfaces