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, Attacks, and Defenses”. Your work assignments Large language model (LLM) agents represent the next generation of artificial intelligence (AI) systems, integrating LLMs with external tools and memory
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medicine in sepsis. The group consists of senior researchers, postdocs, and PhD students with expertise in clinical medicine, cell and molecular biology, proteomics, and bioinformatics. Within the research
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. This project explores using a Radio Digital Twin (RDT) to improve spectrum sensing and enable dynamic spectrum sharing for 6G and future networks. The RDT will model radio propagation and spectrum usage in a
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has gone through a very rapid development in the last few years. Large-scale machine learning models are however notoriously over-confident. With insufficient amounts of data to train them on together
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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for efficient last-mile deliveries with a focus on climate and flexibility. Using advanced modeling and data analysis, you’ll create solutions that make final deliveries smarter and more sustainable. Your work
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also linked to a broad, interdisciplinary network of researchers in several European countries and in the United States, particularly within experimental studies using cell and animal models, with
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thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable; they make sense
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studies using cell and animal models, with the aim of increasing knowledge of the biological mechanisms that may explain epidemiological associations observed in the SELMA study. Link to article Within