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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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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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, 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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• 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
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leveraging advanced computer vision and deep learning-based pose estimation from football match footage to analyze pre-injury biomechanical patterns and joint load dynamics. The research aims to create
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, using methods of applied econometrics and increasingly also machine learning. Large data sets typically form the basis of our analyses. Thus familiarity and a certain expertise on these is also expected
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) Advanced statistical evaluations (in particular machine learning-based analyses and research syntheses such as scoping/systematic reviews, meta-analyses and meta-science approaches) Leading functions in data