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Field
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, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and supported by the COMMLab , the 6GSPACE Lab , the HybridNetLab
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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systems onto real robots for tasks such as tracking, 3D reconstruction, object recognition, and visual SLAM. They will be working with a team composed of PhD students, Research Assistants, and Postdocs
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associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered
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internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
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) Social Media Analysis (iii) Algorithmic Privacy, (iv) Analysis of Academic Collaborations, (v) Human-Bot interaction, (vi) Network Science. The ideal candidate is self-motivated and hard-working with a PhD
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relevant expertise: A PhD in Computer Science or a closely related field, with specialization in Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph
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that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases Developing statistical, graph, and/or machine learning models to study
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candidate is self-motivated and hard-working with a PhD in Data Science, Computational Social Science, Computer Science, or Information Science. The position requires experience with at least one of the
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Developing statistical, graph, and/or machine learning models to study transaction networks, illicit transaction patterns, and DeFi protocol operations Creating and evaluating tools, documentation, and open