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translated into the underlying mathematical models. They may, for example, arise from perturbations in boundary conditions, input parameters, or geometrical properties. When neglecting the influence
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modeling, geometric deep learning or physics-informed machine learning, or you are willing to learn these quickly; strong collaboration skills: you enjoy working in a multidisciplinary team and feel
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metrology tools is continuously challenged by stricter requirements and increasingly complex geometries. In this project, you will work on the ideation, modeling, and experimental validation of novel
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to investigate how cartilage microtissue growth dynamics, matrix composition, and (anisotropic) matrix architecture are influenced by the mechanical and geometric properties of their environment
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, matrix composition, and (anisotropic) matrix architecture are influenced by the mechanical and geometric properties of their environment. These computational models can provide crucial mechanistic insights