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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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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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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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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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-related data together with experimental and clinical collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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Mathematics, or a related field A strong background in image/signal processing, particularly in computer vision. Strong programming skills and experience with at least one deep learning framework e.g
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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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Mathematics, or a related field A strong background in image/signal processing, particularly in computer vision. Strong programming skills and experience with at least one deep learning framework e.g