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Field
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research the dynamic processes of the Earth system. Our vision: “Taking the pulse of the Earth to safeguard a habitable planet.” For section 2.5 Geodynamic Modeling (department “Geophysics”), we are looking
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | 3 months ago
fields Experience with image analysis, or computer vision Good knowledge of basic machine learning techniques, such as variational autoencoder Good presentation and writing skills Proactive, independent
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European partners, and short research stays at TWIN-X institutions in Greece, Italy, France, the Netherlands, Bulgaria, or Switzerland. Research vision The goal is to develop foundation-model architectures
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German Cancer Research Center in the Helmholtz Association (DKFZ) | Oettingen in Bayern, Bayern | Germany | 3 months ago
provide a dynamic environment which empowers excellence with state-of-the-art technologies, cutting edge infrastructure, and a global scientific network. Contribute your knowledge, vision, and dedication
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Top-ranked Master's degree in robotics, computer vision, system control, machine learning, mathematics, or a related field (background in any of the following); Being excited to make a real impact with
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profile is shaped in particular by five high-profile areas. About Minds, Media, Machines Minds, Media, Machines (MMM) is one of the five interdisciplinary, high-profile areas that largely define
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