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computational mechanics of structures. Strong foundation in shell theory, elasticity, and variational mechanics Experience developing custom numerical solvers or using advanced finite element platforms (e.g
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(e.g., finite element or wave propagation simulations) for defect detection and materials analysis Integrate AI, machine learning, and robotics into NDE and manufacturing processes for automation and
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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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microscopy, optical interferometry, vacuum technology, finite element method simulations will be involved. Applicants should hold a PhD in Physics, Nano-science, Engineering or similar, experience with optics
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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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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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. Ability to perform finite element simulations using software such as COMSOL, ANSYS, or ABAQUS. Experience in utilizing these tools for in-depth analysis is highly desirable. Required License/Registration
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, single-atom catalysts), analytical instrumentation (including electronics and LabVIEW FPGA programming), finite element modelling using COMSOL Multiphysics, and in-situ/operando spectroscopy, among other
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. Extensive experience in the development and application of finite element method (FEM) or comparable methods for AM applications. Preferred Qualifications: Demonstrated expertise in multi-physics simulations
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, during neuron differentiation and stress response. By applying a model from epidemic spreading, you will develop a new mathematical model of actual molecular mRNA-protein mechanisms. By combining