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Field
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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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required: Contributions in the area of scientific computing and Computational Fluid Dynamics. Prior Experience in turbomachinery or the Lattice Boltzmann method is an advantage. Operational skills : Physical
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Description of the offer : The condensed matter theory group at Institut Néel (CNRS, Grenoble) invites applications for a three-year PhD position in condensed matter physics starting from October
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. Requirements: We are looking for a highly motivated candidate with a PhD in theoretical/computational physics or applied mathematics. A strong background in quantum mechanics and numerical simulations is
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" (ARMS) code, running on high-performance computing centers. The simulation domain will consist of a restricted portion of the solar atmosphere. The doctoral student will have to explore the simulation
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2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Context
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to the team on national high-performance computers (e.g. Jean Zay @ IDRIS, ADASTRA @ CINES) to carry out numerical simulations. The doctoral student will be registered at the École Doctorale d'Astrophysique
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job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD topic is funded
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3 Sep 2025 Job Information Organisation/Company CNRS Department Grand Accélérateur National d'Ions Lourds Research Field Physics Researcher Profile Recognised Researcher (R2) Country France
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 12 hours ago
neural networks: Where we are and what’s next." Journal of Scientific Computing 92.3 (2022): 88. - Karniadakis, George Em, et al. "Physics-informed machine learning." Nature Reviews Physics 3.6 (2021): 422