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for realizing the physical learning paradigm developed at AMOLF. You will integrate these memristive devices with the reconfigurable nonlinear processing units (RNPUs) developed in the NE partner group to harness
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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? No Offer Description This position is part of the NWO KIC Smart Materials project, Smart Materials for Information Processing, in collaboration with the NanoElectronics (NE) group at the University
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novel strategies to treat seizures and cytotoxic edema, especially in neonates. To reach this goal, we are addressing the following questions: 1. How does the brain inhibitory system work at the cellular
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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Sadron Institute. The team works on themes related to the process-structure-property correlations of pi-conjugated materials for applications in organic electronics. The SYCOMMOR team has particular
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. Machines must be equipped in-situ with smart sensors and supported by systems that can process such as images and time series, in real time. Machine learning and AI have become essential for driving
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learning, or signal processing; familiarity with microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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, or a related discipline. Hands-on research experience in one or more of the following areas will be considered an advantage: Confocal microscopy and Image processing Optical bench instrumentation