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-signalling/ ), led by Prof. Alexander Skupin, which focuses on multiscale analysis of brain disorders with a focus on Parkinson's and Alzheimer's disease, and epilepsy by combining experimental and
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in developing new tools to understand the nervous system and to explore theories behind neural phenomena. As for developing new tools, we have been working on network alignment algorithms [FCC+21] and
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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at the Bachelor's and Master's level, contributing to the delivery of interdisciplinary courses on human rights For further information, please contact Prof. Dr. Robert Harmsen: Your profile Ph.D
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revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria
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-signalling/ ), led by Prof. Alexander Skupin, which focuses on multiscale analysis of brain disorders with a focus on Parkinson's and Alzheimer's disease, and epilepsy by combining experimental and
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. The monitoring of telecommunications and energy production and distribution networks are characteristic examples of such time-critical applications. The project aims to propose unsupervised online CPD algorithms
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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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. Key examples include PubChemLite , MassBank , and Shinyscreen . Further examples are on GitLab , our website and publications . For further information, please contact Prof. Emma Schymanski (email