191 parallel-computing-numerical-methods-"Prof" positions at Technical University of Munich in Germany
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21.12.2021, Wissenschaftliches Personal The Department of Computer Science, Technical University of Munich, has a vacancy for a PhD candidate/researcher position in the area of efficient algorithms
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of acquisition, organization, compression, analysis, and visualization of georeferenced or geometric data in large scales. We put emphasis on methods of distributed computing, machine learning, image and text
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of Munich (www.cda.cit.tum.de ). Accordingly, we are currently searching for a Ph.D. Student to join our team to work on Design Methods for the European Train Control System! Our Research The European Train
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its rich information content, conventional analysis methods have not yet fully realized its potential. This research project aims to develop a robust AI foundation model based on modern Transformer
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of the doctoral project is positively evaluated after the first two years. CMS’s inter-disciplinary team is performing research in the broad field of computational methods for the built environment. Particular
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details for 2 references. If you have questions or require more information, please contact Prof. Bienert: Technical University of Munich Crop Physiology Prof. Dr. Patrick Bienert Alte Akademie 12, 85354
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to the road? Then this position is just right for you! About us In the Autonomous Vehicle Lab, we develop the vehicle of the future with intelligent algorithms and methods. We are involved in numerous projects
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infrastructure for research Excellent training and career support opportunities (courses, personal coaching, ...) Your qualifications Master’s degree in Computer Science or a similar field Good theoretical
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under civil law in Freising, is a research institution of the Leibniz Association that combines methods of biomolecular basic research with analysis methods of bioinformatics and analytical high
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and