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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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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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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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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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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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, including data science techniques, to assess health, welfare and resilience of animals. Your team ADP comprises over 50 members from diverse international backgrounds, including 16 scientific staff members
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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