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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 4 hours ago
dozen passes are regularly made. One of the challenges of the project is to propose numerical approaches that enable these simulations to be carried out within a reasonable time frame, given the volumes
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available in the Department of Physics at the University of Idaho. The primary focus of this position is advancing numerical relativity methods for compact binary systems. Specifically, the scholar will
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Context Statement Position Overview A postdoctoral position is available in the Department of Physics at the University of Idaho. The primary focus of this position is advancing numerical relativity methods
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metadynamics methods. The numerical protocol will be set up on local machines, and data production will be carried out on national supercomputers (CINES, IDRIS). The results of the calculations will then be
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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. The future junior professor is expected to address key questions in the area of numerical methods for partial differential equations. Participation in the research activities of the Faculty of Natural Sciences
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to develop strong skills in viscous fluid mechanics, numerical methods and applied mathematics. Start date: October 2026. Duration: 4 years. Education: A strong 4-year degree or MSc degree in Mechanical
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Electrode Assembly. J. Power Sources 2021, 512, 230431. https://doi.org/10.1016/j.jpowsour.2021.230431. ; [2] Carral, C.; Mélé, P. A Numerical Analysis of PEMFC Stack Assembly through a 3D Finite Element
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7 Jan 2026 Job Information Organisation/Company Johannes Kepler University Department Institute of Numerical Mathematics Research Field Mathematics » Other Researcher Profile First Stage Researcher
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Description One or possibly two postdoctoral positions to work on the theory of generative AI—particularly diffusion models and their generalizations—combining methods rooted in statistical physics and