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constrained-mixture (finite element) models for cardiovascular tissues towards simulating cartilage G&R. You will then simulate cartilage microtissues growing inside engineered, confining microenvironments and
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to the development of next-generation AI-enhanced finite element methods for robust structural design. You will be part of a dynamic and internationally oriented research group with strong expertise in solid mechanics
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AI-enhanced finite element methods for robust structural design. You will be part of a dynamic and internationally oriented research group with strong expertise in solid mechanics, constitutive
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of the finite element method (modelling assumptions, boundary conditions, mesh/element choices, convergence checks). Experience with at least one FE tool such as Abaqus or similar. Scientific programming skills
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materials, device designs, and operational modes for these bioelectronic implants. You will also develop and validate finite element models (COMSOL Multiphysics) to support the experimental work. The work is
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of the position. The successful candidate will have a solid theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization
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of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Knowledge of constitutive modelling of materials. Knowledge of non-linear finite element methods. Knowledge
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until the grant's contract is done; proven previous experience with articles published in first quartile journals (Q1) on the Discontinuous Galerkin Finite Element Method (DGFEM); experience implementing
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of the (computational) mechanics of solids and the finite element method and/or spectral solvers Practical experience in at least one programming language (preferably Python) and experience with the use of Unix/Linux
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programming skills (C++ or Python) and experience with numerical modeling (for instance, Finite Element Analysis or Computational Fluid Dynamics); A strong interest in—and willingness to learn and perform