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Field
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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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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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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redundancy in brain signals elicited by acoustic stimuli. The PhD project falls under Research Track 1: Informational Modes of Learning. Functional connectivity captures the statistical relationships between
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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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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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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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learning, non-Hermitian systems The Quantum AI lab at ETH (Prof. Juan Carrasquilla ) invites applications for PhD positions to work at the intersection of computational quantum many-body physics, machine
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap