41 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" uni jobs in Luxembourg
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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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interruptions by integrating data collection tools, machine learning models, serving infrastructure, and interactive dashboards. Within the team, your main responsibility will be to develop a risk assessment
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in the field of sustainable finance Teach in core areas of banking law. The ability to contribute to teaching in related areas of commercial law—such as financial and securities law, company law
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, and IoT/5G on organisations from both the private and public sectors
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conducts research on the application and the impact of emerging technologies like DLT/Blockchain, GenAI, Natural Language Processing, Machine Learning, Human-Computer Interaction, and IoT/5G on organisations
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, and information extraction. The position is linked to the Interdisciplinary Research Group in Sociotechnical Cybersecurity (IRiSC-https://irisc-lab.uni.lu/ ) within the Interdisciplinary Centre
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, and IoT/5G on organisations from both the private and public sectors
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technologies that have a positive impact on society. For further details, please visit our website: https://www.uni.lu/snt-en/research-groups/finatrax/ The candidate will support project partnerships with
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approaches - such as machine learning, artificial intelligence, or other data-driven methodologies - will be an asset. This position is part of the University of Luxembourg's tenure-track scheme, which offers
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develop machine‑learning models that learn from and build upon these pNTA results. The successful candidate will be supervised by Prof. Dr. Emma Schymanski and Dr. Federica Piras. For further information