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many-body theory, solid-state physics, ab initio methods, and programming experience for numerical methods Specialized knowledge of ab initio Green function methods for bulk materials, surfaces
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computational astrophysics, including experience in numerical methods and machine learning. Research experience at the postdoctoral level, with an established record of publications and impact appropriate
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at the frontline of the rapid developing field of spatial omics. Opportunities for professional development and career advancement. A collaborative work environment with numerous partners. Access to state-of-the-art
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. Specific Requirements Essential skills: • Experience in programming languages commonly used in scientific computing, such as Python and C/C++ • Experience with numerical methods, signal processing
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matrix could better resist to fire than the ordinary concretes [9,10], however no numerical simulation has been carried out to confirm. Description of the work: The PhD thesis includes two phases
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks for numerous retail, restaurant and performing arts discounts, savings on local child care centers and special
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team! For more information: https://www.csus.edu/student-life/housing/ Minimum Qualifications Abilities: Ability to read and write at a level appropriate to the duties of the position; follow verbal and
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capital assets and invites you to join the dynamic team! https://www.csus.edu/administration-business-affairs/facilities-management/ Minimum Qualifications Abilities: Ability to read and write at a level
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. Skills: • Strong programming skills (e.g., Python, MATLAB, or similar). • Proficiency in numerical methods for coupled differential equation systems. • Excellent written and verbal communication skills
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Ecole Polytechnique, in Palaiseau, France, and will consist of theoretical and numerical modellng. The thesis will consist of modeling turbulence using Machine Learning methods, in particular Physics