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Field
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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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associated numerical methods and AI, will be used with High Performance Computing (HPC) to improve understanding of key flow physics and inform future HPT design. Skills and Experience Required: Applicants
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profile Completed university studies (Master/Diploma) in the field of Physics (Computational-, Plasma Physics, Optics) or related field Mastery and use of the scientific method Experience in numerical
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marine sciences, biological oceanography, ecology, or computer sciences. Strong analytical, numerical and practical skills are essential. Experience in coding or applying quantitative methods in a
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Boltzmann method simplifies the numerical treatment of fluid flows by evolving particle distribution functions across straight lines on a computational grid, rather than evolving the trajectories of the flow
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with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage. Experience and abilities
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. Experience with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage. Experience and abilities
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
. Such structures can be used to construct artificial cell mimics and new materials. A scaffold-free DNA tile assembly is a programmable method for the formation of two- and three-dimensional DNA structures which
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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
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to the proposed control methods in respect of heat transfer and drag. *Keywords : flow control, heat transfer, wall-bounded flow, thermal boundary layer, numerical simulations, reduced order model, machine learning