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Postdoc Position at the Research Group Data Mining and Machine Learning at the Faculty of Computer Science, University of Vienna under the supervision of Prof. Claudia Plant. The Faculty of Computer Science
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quantitative methods Experience in learning methods of Computational Communication Science, e.g. computer-assisted text or image analysis, agent-based modeling and simulation, or network analysis. Programming
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-supported methods for creating laboratory reports as a key component of learning scientific writing. Implementing computer-based experimental techniques. Training student assistants and tutors. Keeping up
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the network architecture need to be to capture the solution accurately? In essence, we’re exploring the frontier between modern machine learning and classical mathematical theory—where neural networks meet some
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flows such as entropy dissipation. This is a chance to tackle cutting-edge mathematical and computational problems with real-world relevance, using modern approximation theory and machine learning
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, Synthetic Data for Machine Learning in Privacy Research, Formalization of Security Risk Management, and Security and Privacy of Blockchain Technologies. In the long term, we are concerned with understanding
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The Computational Drug Discovery and Design Group (COMP3D, https://comp3d.univie.ac.at/) at the Faculty of Life Sciences focuses on developing computer-based methods, particularly those involving machine learning and
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ability to express yourself both orally and in writing Computer literacy (MS-Office; Imaging Software) Basic experience in academic writing Didactic competences / experience with e-learning Excellent
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• Computer literacy (MS-Office; Imaging Software) • Basic experience in academic writing • Didactic competences / experience with e-learning • Excellent command of written and spoken English (C1 Level
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profound knowledge of current statistics and omics analysis methods and an understanding of the common fragmentation mechanism of analyzed biomolecules, and current statistics, including machine-learning