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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate
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measurements, and load measurements. Proficiency in computational methods, such as finite element analysis (FEA) and computational fluid dynamics (CFD). Experience with data acquisition and analysis using
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finite-element simulation and topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks above, the
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with finite element modeling (FEM) and computational simulations. Microfabrication & Electrical Characterization: cleanroom fabrication and electrical testing of nanodevices. What We Offer: A dynamic
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models using experimental data for precise mapping of real processes Conducting detailed analyses of thermomechanical stresses in electrochemical converters using the finite element method (FEM
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metal-organic frameworks, 2D/hierarchical structures, nanoparticles, single-atom catalysts), analytical instrumentation (including electronics and LabVIEW FPGA programming), finite element modelling using
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engineering discipline Experience and skills · Proficiency with finite element modelling and FEA software like ABAQUS Explicit or LS-DYNA · Experience in impact modelling or continuum damage
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campaigns; - Process data; - Model using finite element simulations where possible; - Participate in various ANR LASERSHOWS projects; - Supervise trainee and assist PhD student; The person recruited will be
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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
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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