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, development, and training of machine learning and deep learning algorithms. Creation of accurate, robust, and energy-efficient models. Development of systems capable of predicting and making decisions in real
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Hilbert spaces, higher information rates, enhanced security, and increased robustness to noise. This project aims to advance high-dimensional quantum information both fundamentally and experimentally
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demonstrations of robust SOT switching in fully vdW TI/FM stacks [2] and the broad outlook for 2D-material-enabled MRAM motivate this work (see for example our recent Perspective [1]). The selected PhD student
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and refine the RG-based model to enhance its biological interpretability and robustness across different tumor types; to extend the model to simulate and predict solid tumor response to innovative
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and validating architectures tolerant to faults and interference (jamming/spoofing) and robust to radiation-induced degradation, adaptive beamforming, and AI-based radio control. The role spans models