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challenge. This project will explore how reconfigurable computing devices can be integrated into large-scale heterogeneous infrastructures, which include CPUs, GPUs, DPUs, and others. The research will focus
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. The focus will be on designing reproducible techniques for identifying dependency structures, validating these methods through large-scale experimentation, and evaluating how well DNS configurations align
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such cases, maps must be created or transformed from data rather than simply retrieved. The GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo
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regenerative, profitable and socially supported agricultural sector’ in which a large consortium takes up the mission to accelerate to the transition to a regenerative food system. Together with industry
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Master’s degree in Data Science, Artificial Intelligence, Computational Linguistics, Computer Science. Has excellent academic writing and oral skills in English. Has experience with large language models
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in this kind of environment will result in models too large to be handled and too instable to be solved. Data-driven approaches need to be used in addition to enrich the physics-based models
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Application deadline: 23 September 2025 Apply now Are you eager to advance research at the crossroads of geography, data, and AI? As a PhD candidate in the ERC-funded GeoTrAnsQData project at Utrecht
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Why apply? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda
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are not designed to produce reliable regional estimates of those phenomena. Therefore, small area estimation (SAE) methods are used. With technological advances, Big Data now offers valuable spatial
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agriculture. From precision farming and smart irrigation to data-driven decision-making, technology opens avenues for increased productivity, resource optimization, and environmental stewardship. However, while