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(ER-C) and to the Jülich Supercomputing Centre. Within IAS-9, the Deep Learning for Electron Microscopy group develops machine learning methods grounded in the physics of the measurement rather than in
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. You have a solid background in machine learning; experience with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills
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Consiglio Nazionale delle Ricerche-Istituto di Calcolo e Reti ad Alte Prestazioni | Napoli, Campania | Italy | 21 days ago
, and Data Mining techniques; Data preprocessing, feature selection, classification, and clustering; Programming languages and libraries for developing machine learning and deep learning applications
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Biology, Computer Science, or a related field is required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras
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algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems. The project focuses on the intersection of deep reinforcement learning
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events and the climate. The doctoral researcher will be working on the AI-Dream project. They will have to develop criteria and evaluation suites to test the “physical” accuracy of deep Machine Learning
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initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data
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, denoising, and fusion, using vegetation samples or scenes. The student can build upon the team's previous work which uses deep learning for image fusion or employs hybrid fusion methods combining
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degree of autonomy. Influences others regarding policies, practices and procedures. Essential Functions: 60% of Time the Research Scholar may: • Lead development of deep learning algorithms capable of
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Track Record: At least two first-author research papers at flagship venues of core machine learning research (e.g., NeurIPS, ICML, ICLR, AISTATS). Theoretical Rigor: A deep understanding of