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Field
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heating and cooling, 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
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from renewable electricity and sustainable raw materials, represent a promising solution, enabling deep decarbonization. DESIRE is a Marie Sklodowska-Curie doctoral network that aims to train 15 doctoral
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of the bioprinting process. Objective 2: Training of a deep learning model to predict inputs that will achieve bioprinted scaffolds with the required print fidelity and scaffold micro-architecture. Objective 3
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into the generation process. This multidisciplinary project will deliver deployable models, reproducible methods, and, where allowed, shareable datasets. The student will gain training in deep learning, AI, image
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learning, image analysis, and advanced computing to study relationships between structure and function. Keywords: Human Brain, 3D Atlas, Deep Learning, Temporal Lobe, Brain Function Entry Requirements
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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at the intersection of AI and advanced electron microscopy. The project focuses on developing novel self-supervised and physics-informed deep learning methods to restore and denoise Transmission
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, Artificial Intelligence, Machine Learning or Cybersecurity or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in, for example, Machine Learning, Deep Learning
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experience in artificial intelligence and machine learning is expected, but not required. Knowledge and experience in deep learning and generative AI is considered an advantage. Undergraduate research
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working