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of distributed characterization of hollow core fibres, including properties such as attenuation, polarization, but also pressure and content of air inside the fibres. The successful candidate will be based
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of distributed characterization of hollow core fibres, including properties such as attenuation, polarization, but also pressure and content of air inside the fibres. The successful candidate will be based
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of distributed characterization of hollow core fibres, including properties such as attenuation, polarization, but also pressure and content of air inside the fibres. The successful candidate will be based
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Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm
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of algorithms for natural language understanding, structured command generation, and domain-specific model adaptation or fine-tuning. Integrate NLP and speech technologies (ASR/TTS) into a unified tactical signal
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. The post is part of the project that aims to develop digital twin frameworks within the Wind Energy sector with novel AI predictive maintenance algorithms for fault-tolerant, intelligent estimation
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. This role will contribute the development of novel machine learning algorithms for wireless sensing and communication and the proof of concepts for next-generation wireless communication, collaborating with
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. The post is part of the project that aims to develop digital twin frameworks within the Wind Energy sector with novel AI predictive maintenance algorithms for fault-tolerant, intelligent estimation
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work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through
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resiliency, and energy management algorithm development using MATLAB/Simulink for marine microgrid applications. Knowledge on control is highly preferred. Have experience and commitment to supervising student