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Circuits, Robust and Efficient Mapping of Quantum Algorithms on Quantum Machines, Quantum Noise-Aware Optimizations for QML, QML Security, Error Correction for Quantum Computing, Secure Quantum-Classical
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, telecommunications or related field. Other requirements include Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design
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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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on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems tailored to dynamic, human-centered environments. They may also work with diverse signal modalities, including vision
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Tools for Quantum Circuits, Robust and Efficient Mapping of Quantum Algorithms on Quantum Machines, Quantum Noise-Aware Optimizations for QML, QML Security, Error Correction for Quantum Computing, Secure
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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mathematical underpinnings of modern data science and AI. Areas of interest include, but are not limited to: Probability and statistics for data Science and AI Optimization, algorithms, and computational
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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are not limited to: Probability and statistics for data Science and AI Optimization, algorithms, and computational mathematics Information theory and learning theory Other foundational areas
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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