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theory, modeling, and AI-assisted optimization activities within the consortium. Reporting and dissemination of the results. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1074
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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processes, regularity theory of nonlinear degenerate and singular elliptic and parabolic PDEs, free boundary problems, optimal control of free boundary systems with distributed parameters. Current areas
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but not limited to: o Quantum error correction, fault tolerance, and resource optimization. o Entanglement dynamics, quantum control protocols, and hybrid quantum-classical algorithms. o Modeling and
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involved in the project. 2. Participation in the selection of raw materials, optimization of mixtures, and conducting experiments. 3. Analysis of results. 4. Preparation of scientific articles. 5
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investigation in a real or controlled environment. The project falls within the "Nanostructures Axis". Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7374-DANFOR-003/Candidater.aspx Requirements
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on the development and analysis of continuous and discrete models in connection with convex and nonconvex optimization problems and monotone inclusion systems. Our ideal candidate already has experience with modern
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adaptive systems that jointly optimize morphology and control for real-world physical interaction. Requirements Ph.D. in Computer Science or a related discipline. Strong background in Machine Learning
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of singularities, optimal control, and applications to macroscopic models of traffic flow. Teaching may be required. Education and Experience: Applicants must have recently completed a Ph.D. and have exceptional
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managing supercomputer resources Strong skills in algorithm development for large sparse matrices Excellency in programming GPU accelerators from all major vendors Very good command of written and spoken