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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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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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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
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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
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in wireless communications and networking Background in AI and machine learning is an advantage. Experience and skills Knowledge of random-access protocols (e.g. IEEE 802.11 family). Understanding
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both the private and public sectors