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Field
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Physics or Materials Science. Experience on numerically modeling light-matter interactions, designing photonic devices, and employing optimization and inverse design approaches. Experience in project
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Full time: 35 hours per week Fixed term: 36 months (with possible extension to 48 months) We are looking for a talented and ambitious early career researcher to work on the project “Numerical
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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at the intersection of numerical linear algebra and advanced HPC. The candidate will join an international environment, with opportunities to collaborate with experts from the USA, and KAUST and publish in top-tier
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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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numerical validation of error bounds for model-order reduction based on optimal transport, in the context of electronic structure calculations. The postdoctoral researcher will be assigned to the Besançon
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[map ] Subject Area: Applied and Numerical Analysis Appl Deadline: 2025/09/15 11:59PM (posted 2025/08/05, listed until 2025/12/31) Position Description: Apply Position Description Doctoral and
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characterization of germanium detectors, develop advanced Monte Carlo models and AI-driven analysis tools to optimize detector response, and to promote precision medical imaging and low energy dark matter searches
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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
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programme will proceed in three main phases. In the initial phase, you will develop and optimize physical and numerical models describing the electron optics of the complete probe-forming column, including