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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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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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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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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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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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motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service
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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 generation
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of Dr Benoit Gosselin (Université Laval), Guillaume Lajoie (UdeM) and Marco Bonizzato (Polytechnique). It integrates the use of machine-learning approaches to optimize neurostimulation, automation
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-to-system solutions to prepare and submit your application to Grants.gov and track your application in eRA Commons. Learn more . Table of Contents Part 1. Overview Information Part 2. Full Text