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collaborative project that spans multiple continents. Your main role will be to develop advanced algorithms for multivariate, multi-resolution time series analysis of wearable and neurophysiological data spanning
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to the sustained development of the Internet by providing cost-effective cybersecurity and privacy technologies. This will equip IoT infrastructures with the necessary protection levels for the sustainable
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partners. The primary objective of MishMash is to create, explore, and reflect on AI for, through, and in creative practices. MishMash researchers will investigate AI’s impact on creative processes, develop
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. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also expected
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biochemists developing the labeling agents, data analysts developing analysis algorithms and physicists developing hardware. The candidate The candidate should have a firm base in in vivo imaging and cell
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the 01.02.2026 at the following conditions (PhD position): 50% = 19,92 hours Pay grade 13 TV-L limited 30.11.2028 Your tasks: Develop AI algorithms for real-time fault detection, fault classification
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(CAITE) whose aim is twofold. First, it aims to bring AI at the edge, by providing scalability and resource efficiency through the development of cooperative, distributed AI algorithms, optimising data
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electrochemical phenomena. · Propose non-invasive diagnostic methods based on this digital twin. · Develop algorithms to speed up or standardize the determination of the parameters of this digital
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refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role The successful candidate will join the SIGCOM Research Group, led by Prof. Symeon Chatzinotas. This PhD project aims to develop
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Offer Description The successful candidate will be expected to work primarily on aspects related to precision predictions for the LHC and parton-shower simulations. The project encompasses the development