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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 2 months ago
fracture approaches # Documentation, analysis and publication of results in peer-reviewed journals and at international conferences Your profile # Completed PhD in Materials Science, Physics, Materials
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located in the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Our deep-rooted commitment to excellence, collaboration, integrity, diversity and
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) composites, with experience in testing, manufacturing, or modeling preferred. Working within a collaborative team of PhD-level scientists and undergraduate student, the successful candidate will conduct
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a collaborative team of PhD-level scientists and undergraduate student, the successful candidate will conduct innovative research, publish in scientific journals and conference proceedings, mentor
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various concepts in welding and related mechanical engineering processes and cleaning existing datasets. Creating deep learning models. Using deep learning to create a robust model that predicts welding
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framework, collaborate across multidisciplinary networks (biologists, clinicians, materials scientists, and pharmaceutical researchers), and mentor PhD candidates and junior researchers. Where to apply
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behavior of materials subjected to extreme conditions through hypervelocity impact. You should possess (a PhD (or be close to completion) in a relevant subject area as well as a combination of scientific and
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
and experimental, in the broad field of nonlinear mechanics. Preference will be given to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure
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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science & engineering, physics, chemistry, or a
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, knowledge-driven models and AI-based decision support can be integrated to support resilient and energy-aware manufacturing systems. Special emphasis will be placed on multi-objective optimization, learning