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fundamental mechanisms of neuronal loss to better understand why neurons die or axons are damaged to ultimately establish new strategies for the preservation or restoration of neural tissue. We use multiple
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) experience with neural network training and language model fine-tuning; (2) background in natural language processing, linguistics, and/or human reasoning; (3) strong coding skills; and (4) strong
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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NORPOD is a collaborative postdoctoral program of the Nordic EMBL Partnership for Molecular Medicine . The partnership is a network of four national research centers across the Nordics and the
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approximation theory can be automated by a (neural network) guided search over the action space of standard tools (e.g., Hölder inequalities, Sobolev embeddings, ...). Certain proofs in these fields require
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research questions. This postdoctoral scholarship offers the opportunity to be a part of this AI revolution by developing novel neural network architectures specifically optimized for plant genomic data. Our
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), latch-ups, and the total ionising dose on spiking neural network performance. develop and test fault mitigation strategies, such as spike-based redundancy, reconfigurable neural routing, noise-aware
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recognised track records. CNAP participates in numerous international initiatives and maintains an extensive global network, making it an ideal environment to build your own collaborative connections. CNAP is
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation