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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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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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the pulsed electric field so that all microalgae in the solution receive the electric field for equal durations. The aim is to continue the numerical study (CFD) already undertaken in this field to find
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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