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approach that includes cross-section calculations, the development of Monte Carlo codes, and the advancement of the NanOx model for biological dose prediction. As part of a collaboration with the University
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statistical physics at Department for materials research in extreme conditions. . The appointed researcher will join the project team and contribute to the development of the Diagrammatic Monte Carlo X
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collaboration, dedicated to the direct detection of dark matter. They will contribute to various activities including data taking, data analysis, and Monte Carlo simulations. The candidate will be involved in
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properties of phase transitions involving magnetism, elasticity, dielectricity, etc. by applying and developing computational methods such as classical and quantum Monte Carlo simulations, molecular dynamics
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Monte Carlo charged particle simulations to model energy deposition and generation of secondary particles, and collaborate with a team of researchers to explore heating, melting, vaporization, and general
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-doctoral fellow will work within the framework of the project with the aim of both (1) performing Monte Carlo simulations (with established codes such as GEANT4, MCNP, FLUKA, or others) in order to project
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Monte Carlo methods, analysis and interpretation of data to validate theoretical models, manuscript development, and communication of research at relevant scientific meetings. The successful candidate
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will be supervised by Dr. Ning Wang. The successful candidate will be responsible for AI-driven materials discovery. Candidates with background in molecular modeling (molecular dynamics or Monte Carlo
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experimental high energy particle or nuclear physics, 3) very good knowledge of C++ and Python programming languages and the ROOT data analysis framework, 4) the ability to use Monte Carlo generators
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the BL3 DAQ, running and tweaking Monte Carlo simulations for Nab and BL3, finishing timing studies in Nab using external fast scintillators, and supporting integration efforts of the BL3 DAQ with