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characterization, and/or component validation and verification. Strong background in CAD, finite element analysis, additive manufacturing, integrated computational materials engineering techniques and software
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the fundamental engineering understanding of gas centrifuge systems. The group leverages analytical techniques and advanced computational tools—including finite element analysis (FEA)—to evaluate and predict
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in one or more of: Computer vision and Machine learning in development of AI for anatomical landmark detection and/or finite element modelling 2D/3D image segmentation using deep learning 3D–2D
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the fatigue life of highway bridges by investigating the role of damping under combined thermal and heavy traffic loading. The project involves: Developing high-fidelity Finite Element (FE) models of highway
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design, timber construction, CNC fabrication, robotic assembly, plate structures, semantic data models, finite element analysis, and plugin development. The PhD position (full-time) will span 4 years
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dimensioning and tolerancing, including drawing standard ASME Y14.5. Extensive experience with SolidWorks or equivalent 3D CAD software. Proficiency in Finite Element Analysis using ANSYS, COMSOL, or equivalent
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cryogenic measurements at liquid Helium temperatures - Experience in finite-element simulation of semiconductor electro-mechanical devices. - Strong oral and written communication skills
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for unstructured meshes and/or finite element methods Experience with CFD discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC
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for unstructured meshes and/or finite element methods Experience with CFD discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC
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, fatigue, corrosion, and biofouling ) of subsea structures. Derive limit state functions associated with the failure mechanisms using high-fidelity Finite Element Analysis. Perform sensitivity and