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vibrations), and structural (migration of atoms) effects with an atomistic resolution. This can be achieved by self-consistently coupling molecular dynamics (MD), density-functional theory (DFT), and quantum
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and
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allocation and optimization Joint Communications and Sensing/PNT systems Network virtualization and network slicing MAC techniques/protocols for wireless systems Multi-antenna signal processing Graph signal
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/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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times of Markov chains, random graphs and trees, random matrix theory, stochastic and Lévy processes in infinite-dimensional spaces, free probability, random sphere packings in high dimensions. About the
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Amir Probability and group theoryRandom walks, probability and geometry on groups, harmonic functions, opinion dynamics and other stochastic processes on graphs Gil Ariel Bacterial swarming, collective
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and
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programming languages such as Mathematica. 4. Advanced knowledge of mathematical tools and basic knowledge about classical networks including graph theory highly appreciated. 5. High motivation to conduct