147 machine-learning "https:" "https:" "https:" "https:" "The Open University" positions in Luxembourg
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character. The Faculty of Humanities, Education and Social Sciences (FHSE) (https://www.uni.lu/fhse-en/ ) brings together expertise from the humanities, linguistics, cognitive sciences, social sciences, and
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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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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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, MONAI) Strong interest in image analysis / computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not
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wireless communications systems. For details, you may refer to the following: https://wwwen.uni.lu/snt/research/sigcom The successful candidate is expected to perform the following tasks: Design, analyse and
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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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compliance, as well as downstream verification & validation activities, such as software testing and runtime verification. For further information, you may refer to https://www.uni.lu/snt-en/research-groups
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businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? Describe the main responsibilities of the position. The position is placed
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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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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