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computer science / information systems engineering. In this project, the aim is to bridge the gap between large language models (LLMs) and task automation, enabling natural language interaction, that is, via a
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cybersecurity allowing thus to validate and receive feedback from on-the-field cybersecurity practitioners. As generative AI (GenAI) platforms and large language models (LLMs) are increasingly integrated
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placed on synthesis approaches, such as meta-analytic techniques and the analysis of large-scale health data, to systematically integrate evidence and identify patterns across diverse health outcomes
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large part of the project is dedicated to better integrate automation into multiple facets of cyber-range operation, including the federation of infrastructures to deploy complex scenarios in multiple
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use to break into the IT system. Because attack surface became large and diverse [Rizz20], Automated External Attack Surface Management (EASM) cannot be limited to naive IP address scanning or simply
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the advancements brought by AI, there is currently no tool sufficiently intelligent to fully aggregate and utilize diverse data sources to create a comprehensive and adaptive dashboard for taking
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cybersecurity allowing thus to validate and receive feedback from on-the-field cybersecurity practitioners. As generative AI (GenAI) platforms and large language models (LLMs) are increasingly integrated
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data storage capacity to accelerate research in intensive computing and large-scale data analytics, commonly referred to as Big Data. This characteristic distinguishes the HPC center at the university
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data storage capacity to accelerate research in intensive computing and large-scale data analytics, commonly referred to as Big Data. This characteristic distinguishes the HPC center at the university
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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the