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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 2 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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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The candidate will develop numerical procedures in
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Electrical Engineering or Physics or Materials Science. Experience on numerically modeling light-matter interactions, designing photonic devices, and employing optimization approaches. Experience in project
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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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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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date). Strong background in power systems analysis (OPF, state estimation) and numerical optimization/control. Proficiency with Python/MATLAB and power-system toolchains (e.g., MATPOWER/OpenDSS
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction
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Mathematics, or a closely related field. Design and optimize multimodal LLMs to encode, fuse, and reason over heterogeneous scientific data from diverse modalities such as numerical tables, text, and images
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this question using a numerical approach: [Karashabyeva 2025] proposes a topological optimization for a steady-state heat transfer case, while [Alpar 2024, Alpar 2025] addresses shape optimization in both steady