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generative AI algorithms and translate them into intuitive explanations. Interest in cyber-physical systems (CPS) and eco-cognition modeling. Good understanding of mathematical optimization techniques
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or PyTorch. Interest in explainable AI and contributing to explainability repositories for trustworthy, auditable algorithms. Experience or interest in cyber-physical systems (CPS), eco-cognition, and
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the field of analytical and/or simulation methods for composite materials. You have extensive experience with development and implementation of algorithms for modelling of composites You have experience with
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, exploring multimodal fusion, robust pose estimation, and real-time processing. Together, these roles form a cohesive effort to bridge algorithmic innovation with practical applications in next-generation
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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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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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and Delivery: Design and develop training programs on Data Science and AI topics, including machine learning algorithms, data visualization, and statistical analysis. Provide foundational sessions about
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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