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hydrodynamic and mechanical stimulation using advanced microscopy; - Developing multiphysics models of fluid transport and tissue deformation (COMSOL Multiphysics); - Validating the platform using patient
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become part of a laboratory that covers a wide range of fields, from statistical physics to hydrodynamic turbulence, including mathematical physics and signal processing, as well as soft and condensed
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characterized to be far from thermal equilibrium, such that the governing physical principles have to be reconsidered. We combine modeling, high performance computing, and analytical theories tailored
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of Marine Technology at NTNU has a vacancy for a PhD Candidate in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures. The position is part of the AIMOS project (Artificial Intelligence
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between pollution control efficiency, electrochemical yield, and energy recovery potential; • Develop coupled electrochemical and hydrodynamic models to predict process behavior; • Participate in
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, from fundamental theory, laboratory experiments, and detailed numerical simulations, to mesoscale pore network modeling and upscaling to continuum-scale theories that can be applied in application
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marine datasets in the Baltic Sea region, including in situ observations, remote sensing products, and hydrodynamic model outputs. These datasets can be unified through a geoid-based vertical reference
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The successful candidate will join the Climate Physics team at ENS de Lyon (https://climatephysics-ensl.fr/ ), which currently consists of 5 permanent researchers, 7 PhD students and 2 postdoctoral researchers. We
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-fidelity numerical models to be analysed under regular and irregular sea states (e.g., coupled hydrodynamic–structural models and/or FEM workflows). Develop surrogate models representing the coupled dynamic
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response, infrastructure resilience, and long-term planning. Traditional flood modelling approaches fall into two broad categories. Physically based hydrodynamic models, such as CityCAT, simulate flood