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inequality, (iv) diversity, (v) online controlled experiments, and (vi) network science. The ideal candidate is self-motivated and hard-working with a PhD in Data Science, Computational Social Science
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(PDA) to engage in cutting-edge research on the physical layer design of next-generation wireless communication systems, with a primary focus on 6G technologies. The research will particularly emphasize
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. Strong attention to detail, excellent organizational skills, and exceptional verbal and written communication abilities are essential. The candidate should be capable of working both independently and
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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management Cognitive radio or adaptive communication systems, including dynamic spectrum access, band selection Heterogeneous network architectures, including terrestrial and non-terrestrial networks Deep
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on the physical layer design of wireless communication systems and explore enabling technologies for 6G and beyond wireless networks. Applicants must hold a PhD degree in electrical/electronics engineering
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) Social Media Analysis (iii) Algorithmic Privacy, (iv) Analysis of Academic Collaborations, (v) Human-Bot interaction, (vi) Network Science. The ideal candidate is self-motivated and hard-working with a PhD
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to existing open-source engines. Publish in top-tier conferences (SIGMOD, VLDB, PODS, ICDE, ICDT, etc.) and participate in the wider research community. Mentor students and contribute to a vibrant research
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possible research topics include: (i) social media analysis, (ii) collaboration and teamwork, (iii) gender inequality, (iv) diversity, (v) online controlled experiments, and (vi) network science. The ideal
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models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems (e.g., transportation networks, manufacturing systems, and truck routing). Assessing