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English Proficiency in machine learning and large omics data analysis is preferred. Where to apply Website https://www.lih.lu/en/job/?value=JA/PDGMB0326/MD/DIIA Requirements Research FieldComputer science
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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and uncertainty mapping at satellite, airborne and drone levels. You will explore advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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perturbation modelling. The ideal applicant brings not only strong technical skills, but also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine 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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robotic arms and performing software and hardware integration Mentor and Advise PhD thesis within the research group. Current topics include, reinforcement learning for tactile-aware manipulation skills
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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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closely related field PhD training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years
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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