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and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our
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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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years of post-PhD research and engineering experience in AI for mobile security Solid knowledge in adversarial machine learning or trustworthy AI, including experience with robustness assessment and
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Stability Market, located in Luxembourg. The candidate will join the Security, Reasoning and Validation (Serval) research group and work on a research project related to the application of machine learning
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data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by delivering
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, combining Time-Sensitive Networking (TSN), Software-Defined Networking (SDN), and advanced machine learning approaches to ensure compliance with ISO/SAE 21434 and UNECE WP.29. Respnsibilities: Develop
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, Python)—knowledge of machine learning/data science is a plus; Excellent communication and collaboration skills in an interdisciplinary and international environment; Fluency in English (oral and written
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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machine learning or trustworthy AI, including experience with robustness assessment and attack/defense mechanisms. Expertise in software security and code analysis, with understanding of common
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and