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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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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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, 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
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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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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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research team 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
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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
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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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