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Eligibility criteria The candidate must have a PhD degree in physics (atomic, particle, hadronic or nuclear) with a strong focus on theory. The project requires sound knowledge in theoretical physics, computing
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FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in Physics with strong interest for experimental physics. - Expertise in one of the followings: laser-plasma accelerators
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of Research ExperienceNone Additional Information Eligibility criteria The candidate must hold a PhD in physics or engineering sciences. He/she should have an experimental profile and demonstrate a strong
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theoretical chemistry applied to materials, materials physics, or computer science/applied mathematics; - Solid experience in DFT applied to periodic systems (codes such as CASTEP, VASP, Quantum ESPRESSO, xtb
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correlations. - Analyse experimental data mixing both quantum optics and solid-state physics phenomena - Supervise PhD students and interns - Ensure an efficient collaboration and information sharing with
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Information Eligibility criteria Knowledge Requirements: The candidate (M/F) must have a strong background in advanced quantum mechanics, with a PhD in theoretical physics. A solid understanding of quantum
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of axion production in dense media, with a particular emphasis on gamma-ray signatures, gravitational waves, and multi-messenger observables. Qualifications: Applicants should have: a PhD in physics or
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set by the CNRS scale and commensurate with experience. A PhD in cosmology, astrophysics, or theoretical physics is required by the start date. The start date is October 1st- 2026. Application Procedure
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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physics, engineering or related fields. Solid background in aerodynamics, vortex dynamics and possibly two-phase flows. Experience in theoretical stability analysis and use of asymptotic methods. Knowledge