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, architecture, and/or industrial design. You also possess: Strong analytical skills and innovative attitude Previous experience with finite element simulations and/or rapid prototyping Good programming and image
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
-Lagrangian (CEL), Material Point Method (MPM), or advanced Finite Element Methods). Physical modeling of tunnel excavation and ground response (e.g., geotechnical centrifuge testing, lab-scale TBM experiments
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modeling for neurobiology, including finite element modeling (FEM) or system identification methods in medical applications About the Department The Department of Biomedical Engineering is an academic unit
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, or a closely related field with expertise in one or more of the following areas: Finite element methods for partial differential equations Multiscale numerical methods Flow and transport in porous
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. The candidate should have excellent grades from the bachelor and master level courses in mathematics and mechanics as well as appropriate skills in programming and finite element analysis . Proficiency in
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://www.chalmers.se/math/ At the division of Applied Mathematics and Statistics we conduct research within probability theory and its applications, the theory and implementation of finite element methods, inverse wave
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programming skills and finite element (FE) analysis experience is a requirement. Knowledge of machine learning, Matlab and Abaqus is also highly desired. Previous experience with parametrization and automation
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. The postdoctoral researcher will have the opportunity to: Study the principles of plant cell adhesion and tissue biomechanics. Use microscopy image processing tools and software. Use, refine and develop finite
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Element Analysis for thermo-mechanical fluid-structure interaction analysis. Ability to demonstrate good collaborative skills, including the ability to work well with other divisions, laboratories, and
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(variational multiscale, multiscale finite elements, etc.), structure preserving numerical methods, stochastic optimization, analysis of machine learning methodologies, multilevel methods, scale-bridging and