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) and the German Academic Exchange Service (DAAD) since 2007. Under this CAS-DAAD joint programme up and coming young Chinese scientists from the University of Chinese Academy of Sciences (UCAS) and CAS
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16.08.2023, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at the Technical University of Munich invites applications for the position of a Research Assistant
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Description Within the Collaborative Research Center “Wave phenomena – analysis and numerics” (CRC 1173) we are currently seeking to recruit, as soon as possible, a Doctoral Researcher (f/m/d – 75 %) in Mathematics for the project “Quantized vortices and nonlinear waves” The CRC has been funded...
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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“Light- versus electron-induced spin-state switching of complexes on insulating layers” within the Priority Programme SPP 2491 “Interactive Spin-State Switching” This DFG-funded project aims
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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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that are technically well-grounded and at the same time represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster
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to work in a team and in a research network • Computer programming experience • Knowledge in electronics • Experience in the field of optics and quantum optics is desirable We offer: • A truly unique
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identification of spider mite infestation foci and needs-based beneficial insect application in outdoor cucumber cultivation’, which is funded by the BMBF within the funding programme KMU-innovativ: Bioökonomie
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training