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) , Astrophysics Experiment , High Energy Experimental Appl Deadline: 2025/12/07 11:59PM ** (posted 2025/11/06) Position Description: Apply *** the listing date or deadline for this position has passed and new
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. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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properties (including structural, defect, electronic, ion transport, surfaces) and will link up with parallel experimental activities. Applicants should have a PhD in chemistry, physics or materials science
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to prevent the onset of cancer. In parallel, our research is dedicated to advancing our understanding of bladder tumor evolution biology through the analysis of next-generation sequencing (NGS) data and the
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 1 month ago
infrastructures over the next decades. Seven academic laboratories are involved in TASTING: G2Elab, GeePs, IRIT, CEA-LIST, L2EP, L2S, and SATIE, as well as the Ctrl-A team from Inria. The project also benefits from
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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to this classification. It is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. Summary: The Goodwill Computing Lab seeks a Postdoctoral
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framework (e.g., OpenFOAM, ANSYS Fluent). Solid understanding of heat and mass transfer, turbulence modeling, and reaction kinetics. Experience with high-performance computing (MPI, parallel simulations) is
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, mechanical and durability testing, and integration with advanced machine learning models. The postdoc will collaborate closely with CEBE’s parallel work packages. Experimental and analytical data generated in