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English. A strong background in graph theory and graph algorithms is necessary. For PhD position 1, we appreciate prior mathematical exposure to at least one of the following topics: random graphs
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more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By
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of biomolecules which can only be successfully tackled by employing a variety of different theoretical methods. In this respect, this joint graduate college brings together the expertise in analytical theory from
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, which arise from processes such as hybridization, horizontal gene transfer, and recombination. Creating such networks from DNA sequences requires techniques from graph theory, theoretical computer science
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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ability to work both independently and collaboratively are essential. A solid background in combinatorics, especially graph theory, together with basic knowledge of probability theory, are required
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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep
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ability to work both independently and collaboratively are essential. A solid background in combinatorics, especially graph theory, together with basic knowledge of probability theory, are required
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, tasks have a continuous evolution, and the precedence graph becomes dynamic. There is an initial method proposed in the literature, where a static model is proposed, introducing two states of products
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counterfactual reasoning frameworks that uncover latent mechanisms and enable principled hypothesis testing. Our goal is to advance the theory of representation learning and causal inference in high-dimensional