14 machine-learning "https:" "https:" "https:" "https:" "https:" Postdoctoral positions in Luxembourg
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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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
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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
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/performance trade-offs and typical RAN levers; experience with energy metering data is a plus. • Strong background in AI / Machine Learning for decision-making (e.g., forecasting, optimization with learning
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3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
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/admittance, force control Experience with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g
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training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years of postdoctoral experience
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harmonization, multi-omics integration as well as the development of machine-learning models for patient stratification and outcome prediction. Moreover, complex multi-layered datasets shall be integrated
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machine learning technologies in order to provide evidence-based decision support tools in near real time across a variety of thematic domains: disaster risk reduction, sustainable agri-food systems
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department