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that positively impact society. For more information, please visit our website: https://wwwen.uni.lu/snt/research/finatrax/ . The candidate will be funded through an industry partnership with Luxembourgish Leader
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. The group consists of doctoral and post-doctoral researchers from diverse backgrounds. For more information, please visit our website: https://wwwen.uni.lu/snt/research/finatrax/projects Successful candidate
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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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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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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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on neurodegenerative processes and are especially interested in Alzheimer’s and Parkinson’s disease and their contributing factors. The LCSB recruits talented scientists from various disciplines: computer scientists
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on neurodegenerative processes and are especially interested in Alzheimer’s and Parkinson’s disease and their contributing factors. The LCSB recruits talented scientists from various disciplines: computer scientists
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with the nano-drones of the University’s SwarmLab The research activities will be hosted by the Parallel Computing and Optimisation Group (PCOG) at SnT, headed by Prof. Grégoire Danoy. PCOG conducts
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of Life Sciences and Medicine (DLSM). The successful candidate will establish and lead an internationally recognized, independent pharmacological research programme in an area of biomedical relevance
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware