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of computation that aim to lower energy consumption for machine-learning and information processing tasks (see, e.g., arXiv:2308.15905 ). Quantum phenomena in information processing: Investigating how genuine
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, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally
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on combining innovative technologies such as remote monitoring, large language models, machine learning, blockchain, and eco-accounting to enhance the efficiency, security, and sustainability of e-bike charging
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and a heterogeneous emerging computer architecture, collaborate regarding compiler and other tools as well as modeling their hardware for integration into the emerging computer architecture framework
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-modal representation learning, and self-supervised learning for this novel perception task. The developed models should provide holistic representations of all surrounding traffic by fusing multi
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, Data Science, Machine Learning, or a related field. Experience and skills · Strong knowledge of AI, Machine Learning, data-science (e.g., neural networks, deep learning, autoencoders, GANs, active
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g
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of dissertation topics: Development of Machine Learning Frameworks for Reactive Atomistic Materials Modeling (DSP II) Profile of the graduate This Ph.D. program is an interdisciplinary study combining physical
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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts
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biodiversity support, cooling, air quality regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue