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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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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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learning, large language models, and the theory of deep learning. The candidate will develop DRL algorithms for online and off-line tasks, for robotic applications and possibly for LLM reasoning applications
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/software testbed. Integrate SDRs (e.g., USRPs, RFSoC) and RF front-ends (FR2, FR3, V-band) into end-to-end ISAC demonstrators. Implement and optimize advanced signal processing algorithms for joint
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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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University Abu Dhabi, seeks to recruit a motivated research or postdoctoral associate to lead the design, development, and validation of material, control systems, and algorithms for next-generation soft
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applications for a Post-Doctoral Associate position, in the area of Quantum Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems
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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by