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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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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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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded
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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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Vacancies Postdoc position on Data enhanced physical reduced order Models Key takeaways The position is embedded into a large-scale effort between academic groups in the Netherlands and industrial
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. Dr Chisari’s team engages in major large-scale structure surveys (e.g. LSST DESC, Euclid), and visits to Leiden Observatory to collaborate with the lensing group are planned. Involvement in outreach