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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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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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. 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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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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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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its processing capabilities but also its adaptability, leveraging early developmental data and ecological validity. Infant data will be acquired in collaboration with Dr. Tessa Dekker (University
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Your job Are you a highly motivated and enthusiastic individual with a strong background in process-based modeling, data analysis, and soil sciences? Do you want to participate in a large-scale
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flexibility. To fully unlock this potential, we need advanced tools that digitally replicate these networks and support optimized design and data-driven control strategies. As our PhD candidate, you will
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systems, focussing on the inclusion of thermal aspects. You can leverage a large body of existing research and software for the efficient simulation of complex geometrically nonlinear mechanical elastic
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on correct gene expression, so the process of transcription is tightly regulated. During early elongation of RNA Polymerase II, it is likely to terminate. Early termination has a big impact on transcriptional