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Field
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are developing AI that can map welding processes in the automotive industry based on simulation data. We are looking for a student assistant with an interest in machine learning and finite element simulation. Your
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skills. You’ll have strong skills in Finite Element Analysis (e.g. Abaqus, ANSYS), an understanding of machining processes, and a proactive, collaborative approach to problem-solving. The University
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skills include: Interest or background in composite materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a
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characterization techniques with mechanical behavior and finite element methods. The postdoctoral candidate will develop the processes needed to connect mechanical testing data with 3D microstructure of nuclear
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materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a plus Familiarity with FE simulation tools such as ANSYS
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validation experiments for modelling • Computational fluid dynamics techniques • Finite element analysis method • Reviewing literature, planning and managing research, writing technical report / paper
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application of machine learning to biomedical data ï‚· Background in computational modeling for neurobiology, including finite element modeling (FEM) or system identification methods in medical applications
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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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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 2 months ago
validation; proficiency in Python and scientific computing, including experience with finite element methods, preferably using FEniCS or DOLFINx; strong analytical and communication skills, with the ability
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simulations and finite element analysis, with high-heat flux electron beam experiments. The research will simulate and replicate steady, cyclic, and transient thermal loads to better understand PFM behaviour