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the risks and success rates of real, patient-specific aneurysms, their treatment options, and long-term prognosis. The project is complemented by contributions in machine learning, such as the rapid
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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to an open-ended academic position in which the holder can form a research group, apply for externally funded research as a principal investigator, and teach. In addition: you must have completed all
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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challenges in urban development. You will explore exciting areas such as applying machine learning models to analyze urban mobility patterns, identifying gaps in transport accessibility, and supporting
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on analysing modern and historical textual records through the lenses of graph-network analysis, statistics and machine learning. The topics include: * Topic: Networks in Historical Scholarly/Philosophical
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the lenses of graph-network analysis, statistics and machine learning. The topics include: * Topic: Networks in Historical Scholarly/Philosophical Writings Analysis of late medieval scholarly manuscripts
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systems), Mathematical biology (Dynamics of ecosystems, Animal movement, Epidemic processes, Forest fires, Biological evolution, Modeling based on Machine Learning and Neural Networks), Socio-economic
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machine learning approaches, and genetic approaches to manipulate the expression of candidate genes in microglia in vivo. The project capitalizes on a cell-therapy recently developed in the team, which
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methodologies for analysing RNA modification readouts from large transcriptomic datasets. This position will focus on developing probabilistic deep learning frameworks to identify molecular determinants