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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 3 months ago
://www.cmap.polytechnique.fr/~aymeric.dieuleveut/ ), professor at Ecole Polytechnique (Palaiseau). The successful candidate will implement and compare different distributed and stochastic numerical optimization paradigms
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks for numerous retail, restaurant and performing arts discounts, savings on local child care centers and special
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of this position is to ensure that the Cleanroom & Nanofabrication Facility operates efficiently while maintaining the highest standards to optimize the research productivity and educational opportunities for BC
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study the error-correction capabilities of multimode RSB codes and understand the structure of the dominant noise processes affecting the RSB code. Such an analysis is essential for optimizing error
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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exploit a numerical model that consistently integrates: • frictional slip (aseismic and seismic), • fluid pressure evolution, and • fault permeability variations, in order to analyze the mechanisms
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, within the scope of the R&D project “OPTIMIZED – Optimized interventions for eating disorders prevention and treatment”, operation nº 16211, COMPETE2030-FEDER-00736900, financed by the Fundo Europeu de
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economic development. The role requires strong foundations in mathematics and computer science, including statistical learning, optimization, simulation, and algorithmic reasoning. The successful candidate
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. The applications of the materials studied are numerous: building construction, transportation, energy, health, environment, fashion, and more. This work is part of the ANR TAFFTA project, "Towards high value-added
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take