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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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, Mechanical Engineering, Electrical/Computer Engineering, Physics, or a related field at the time of appointment Required knowledge Excellent knowledge of aeronautical or spaceflight mechanics Excellent
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, Mechanical Engineering, Electrical/Computer Engineering, Physics, or a related field at the time of appointment Required knowledge Excellent knowledge of aeronautical or spaceflight mechanics Excellent
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials
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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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research spectrum covers a unique range. Institute of Hydrogen Technology The focus of the Institutes of Hydrogen Technology and Photoelectrochemistry is hydrogen, an essential building block for the energy
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers