57 algorithm-development-"Helmholtz-Zentrum-Geesthacht" Postdoctoral positions in United Arab Emirates
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learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms at the intersection of robot learning, control, and AI
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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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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research on the design and development of next-generation wireless communication systems. Specific topics include, but are not limited to, waveform design for the THz spectrum under hardware constraints
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Responsibilities The PDA will conduct research to design and develop optical wireless communication systems. This involves the development of mathematical models for signal transmission/reception, derivation
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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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Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum experiments and quantum algorithms for computational geometry problems. Prior
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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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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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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