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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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-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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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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innovative research on marine mammal health and mortality; analysing large datasets from post-mortem investigated marine mammals and related environmental factors; applying advanced data analysis techniques
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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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will combine BERT type models and Large Language Model (LLM) to examine research publications and patents, in combination with other unstructured data sources such as policy documents, media, call
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postdoctoral researcher, you will use various techniques to investigate fiber-reinforced structures in nature (i.e., fungi), including additive manufacturing, computer simulations, materials testing, and
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planning and control concepts to organize customization in secondary education. A large groups of schools and other external partners provide the opportunity for interfacing research and implementation
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emerging autonomous vehicles. The whole project encompasses a broad spectrum of academic, industrial and societal partners and includes a large group or researchers. The postdoc position will advance state