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the Applied and Computational Mathematics group. His research focuses on time integration methods for all types of (coupled) dynamical systems, with a focus on port-Hamiltonian ODEs, DAEs and PDEs with
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the activities of the SFB ChemPrint Writing of scientific publications and project reports Your Profile: Masters degree in materials science, chemistry, physics, production engineering, energy technology or
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strongly encourage qualified women to apply for the position. Your tasks development of novel machine models based on supervised, unsupervised and reinforcement learning that can be combined with, enhance
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welcomes applications from individuals with a master’s degree in the following fields: Computer Science Mathematics Natural or engineering sciences or an equivalent degree, interested in High-Performance
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, Physics, Biotechnology, Microsystems Technology or a related field is required. Experience in one or more of the following areas: microfluidics, microscopy, fermentation, phototropic organisms, cell culture
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Technology, Mechatronics, Robotics, Systems Engineering, Applied Mathematics, Technomathematics, Computer Science, Engineering Informatics, Theoretical Computer Science, Physics Description Description
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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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least one of areas like animal communication research, probabilistic modeling, or language evolution is a strong requirement. As the position involves computational / mathematical modeling in the form
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master’s degree in (applied) mathematics or related fields - at least basic programming skills, such as Matlab/Python - good communication skills and interest in interdisciplinary research/geophysical
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description