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Field
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and numerical models that predict material deformation, force transmission to cells and the resulting changes in cellular mechanics and signalling. The position is primarily computational, but a
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of thermal effects on force generation, material properties, and geometric clearances. Validate numerical models against experimental results and data available in the scientific literature. Disseminate
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disturbances. Current control methods generally rely on simplified interaction models based on constant aerodynamic coefficients, quasi-static approximations, potential flow models, or experimentally identified
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an integrated field and numerical modeling approach. Your tasks are to: - reprocess and jointly model the available regional magnetotelluric (MT), gravity and magnetic data to develop an initial model
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(further funding will be applied for) and focuses on power systems engineering in the context of the next phase of Iceland’s energy transition. It builds directly on established modeling work at Reykjavík
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energy piles have been extensively studied, using field tests, physical modelling, and numerical modelling, the behaviour of energy piles under complex thermomechanical loading (e.g. vertical-horizontal
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numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific computing environment. A fundamental
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-driven modelling. Experience with numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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performed by the PhD candidate will be supported by numerical modelling performed within the hosting team and by a partner team of ANR MultiMAGe. Expected skills. _Required_: 1 - Excellent understanding