41 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" uni jobs in Luxembourg
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: Contract Type: Fixed Term Contract 7 Month Work Hours: Full Time 40.0 Hours per Week Location: Belval Campus Internal Title: Research support technician Job Reference: UOL07824 . Where to apply Website https
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: Contract Type: Fixed Term Contract 7 Month Work Hours: Full Time 40.0 Hours per Week Location: Belval Campus Internal Title: Research support technician Job Reference: UOL07824 . Where to apply Website https
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development. Do you want to know more about LIST ? check our website: https://www.list.lu/ How will you contribute? Join our dynamic team with this exciting job opportunity focused on the development
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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