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working in radiological environment. Experience in heat transfer and thermal modeling/simulation using finite-element analysis (FEA) or other software. Ability to work within a multi-disciplinary team
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record in theoretical and/or computational mechanics Knowledge, Skills, and Abilities Excellent analytical and mathematical skills Proficiency in numerical analysis using finite element method (FEM
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of the project team. Key Skills & Experiences This project will require previous expertise in computational mechanics (preferably finite element methods – with analysis being undertaken in open-source code MoFEM
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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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of the project team. Key Skills & Experiences This project will require previous expertise in computational mechanics (preferably finite element methods - with analysis being undertaken in open-source code MoFEM
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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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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