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of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
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further appears in the approximation of various dynamical problems by tensor networks and neural networks. In all these cases, the parametrization is typically irregular, meaning that the occurring linear
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specific requirements: a) Experience in the application of data analysis and machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification
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neural network have been demonstrated using a single device. The main mission of this postdoc project, financially supported by IMITECH project, is to develop reservoir computing function based on a
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will join a community of exceptional scientists working on diverse topics ranging from how organisms age or how our DNA is repaired, to how epigenetics regulates cellular identity or neural memory
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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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, neuromorphic electronics based on spiking neural networks (SNNs) and the compute-in-memory (CIM) paradigm is emerging as the most promising path. While the research community has so far favored resistive Compute
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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of personhood and identity. Yet, AI technologies (e.g. large language models and deep neural networks) challenge forgetting as the technologies ‘never forget’ and challenge secrecy by collecting data and
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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT