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Field
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(7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics
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in network science. NetSI faculty and researchers are working to advance the understanding of complex systems through the lens of networks, and to apply this knowledge to solve real-world problems in a
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Networks. Preferred Qualifications: ● Experience in quantum network experiments/devices. ● Experience in writing research proposals. ● Experience in supervising/guiding PhD and/or MS students
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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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management to improve the performance of the future wireless communication systems. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we
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Networks. Preferred Qualifications: ● Experience in quantum network experiments/devices. ● Experience in writing research proposals. ● Experience in supervising/guiding PhD and/or MS students
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in network science. NetSI faculty and researchers are working to advance the understanding of complex systems through the lens of networks, and to apply this knowledge to solve real-world problems in a
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complexity of relationships between the nodes of networks with a mul-tidimensional nature, tensors are used to represent these kind of networks. For example, the transport network mentioned earlier would be
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adaptable object handling in complex real-world scenarios, including those involving deformable objects and uncertain conditions.. Semantic Navigation & Mapping – Designing solutions for robot navigation
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communication theory, machine learning, complex networks, and optimization. The employment This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three