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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research
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profile: • Very good degree (Master or Diploma) in aerospace engineering, mechanical engineering, computer science or a comparable field. • Experience in machine elements, structural analysis, fault
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Engineering or a related discipline. Experiences in Machine Learning, Deep Learning and Artificial Intelligence. Strong programming skills (Python) Good knowledge of AI frameworks like TensorFlow, PyTorch
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The Max Planck ETH Center for Learning Systems (CLS) addresses cross-disciplinary research questions in the design and analysis of natural and man-made learning systems. The excellent engineering
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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory
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to obtain a doctoral degree in aeronautics at the Technical University of Munich or the University of the German Armed Forces Munich. Academic Requirements degree in a subject related to aerospace technology
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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group, a multinational insurance company. Tasks Your duties will include: Literature research Designing, implementing, and evaluating novel machine learning approaches to detect building attributes from
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skills in Python and PyTorch including fundamental software engineering principles and machine learning design patterns. Be highly motivated and a team player with excellent communication and presentation