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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model
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include Master’s-level in Mechanical Engineering, Aerospace Engineering, Control Engineering, Wind Energy, or a closely related field. Strong background in control theory, fluid dynamics, structural
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and the ability to develop relevant theories and methodologies. Strong competencies in product assembly/disassembly, manufacturing, platform design, and robotics Experience with Digital Advanced Product
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strain theory and advanced hyper elastic material models incl. anisotropy You preferably have insights into topology optimization and/or finite element analysis and are committed to improving state
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interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and statistics. This PhD project falls under Research Thrust RT3 on representation
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, probability theory, and combinatorial optimization. Experience in decision-making under uncertainty and autonomous system operation. High level written and spoken English skills. You may obtain further
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volatility. CLASSIQUE is organized into four Research Thrusts that rely upon interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and
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: A master's degree in data science/AI, Mathematics, Electrical and Computer Engineering, or a related field. Strong background in machine learning, data science, and optimization theory. Desired
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on novel high-resolution microscopy techniques to experimentally quantify transition probabilities, alongside density functional theory (DFT) calculations of defects in feldspar. This inter-disciplinary
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tools, and learning environments which can bring significant improvements in mathematics and/or computer science at university education, through a collaborative, reflective, and theory-informed research