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limited to): O-RAN based architectural integration of 5G/6G Terrestrial/Non-Terrestrial and Wi-Fi, development of advanced algorithms and optimisation techniques to enhance the performance and efficiency
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integration of 5G/6G Terrestrial/Non-Terrestrial and Wi-Fi, development of advanced algorithms and optimisation techniques to enhance the performance and efficiency of the integrated RAN, spectrum management
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functionality. Your work will involve designing advanced algorithms that efficiently utilize UWB signal features (RSSI, channel impulse response, phase and amplitude data, Doppler maps,..) to support both high
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scheduled on a rolling basis starting November 15. The final candidate will be selected by mid-December. As Ghent University maintains an equal opportunities and diversity policy, everyone is encouraged
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. Interviews will be scheduled on a rolling basis starting November, 15th. The final candidate will be selected by mid-December. As Ghent University maintains an equal opportunities and diversity policy
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University needs to be started between 1 October 2026 and 28 February 2027. At least 24 months of locally funded research in the South must be scheduled in between the first and last research stay at Ghent
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University needs to be started between 1 October 2026 and 28 February 2027. At least 24 months of locally funded research in the South must be scheduled in between the first and last research stay at Ghent
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, no practical exercises are scheduled. You will assist in the internal and external services of the department. WHAT WE ARE LOOKING FOR You hold a degree of Master in Bioscience Engineering, Master in
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reinforcement learning for flexibility exploitation, and design of AI-based energy management algorithms to coordinate individual and combinations of flexible loads. These algorithms should cater for large asset
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with a flexible schedule, as required. WHAT WE CAN OFFER YOU We offer you a contract of definite duration for the period of three months (50% time). Your contract will start on 1/10/2025 at the earliest