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MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless communications Hardware-constrained signal processing
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violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic
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victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic
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learning for wireless communication problems, particularly in areas such as spectrum management, adaptive system design, or cognitive radio. The candidates will be considered until the position is filled
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models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations and/or experimental
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learning theory to join the research team of Prof. Muhammad Umar B. Niazi. The position focuses on the design and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems
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., 2019; Pedersini et al., 2023). We combine ophthalmological, neuroimaging and behavioral data, and incorporate deep learning methods to facilitate biomarker discovery and enhance predictive power. As a
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collaborators in material science. The developed technologies will be utilized to develop digital twin and soft robot-assisted simulations in the areas of brain machine interaction, wearable haptics, and
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, band selection Heterogeneous network architectures, including terrestrial and non-terrestrial networks Deep learning for wireless communication problems, particularly in areas such as spectrum management
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have at least a bachelor’s degree in biology with several years of experience in neuroscience, or other related fields from an accredited institution of higher learning, or have more than five years