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expose the successful candidate to cutting-edge genome editor engineering approaches and the delivery of these reagents in vivo via AAV or lipid nanoparticles. The successful candidate will also learn
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 8 days ago
the Fugger lab at the Oxford Centre of Neuroinflammation , focusing on the development of drugs that tame common brain diseases through the application of graph-based neural networks, deep learning, and
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As a PhD candidate, you will: - Develop and train deep-learning
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interested in using AI to unravel the mysteries of the brain? Do you want to perform cutting-edge NeuroAI research and leverage deep learning to understand human vision? Then check out the vacancy below and
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learning, particularly deep learning and physics-informed methods, offer transformative opportunities to redesign how data are acquired and reconstructed, and how physiological parameters are inferred from
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and reinforcement learning approaches that adapt lineup presentation in real time based