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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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Monte Carlo and X-propagator techniques. The application must include: proof of the required qualifications (copy of PhD diploma or certificate), a motivation letter outlining scientific interests and
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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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graduate and undergraduate students. Job Requirements REQUIRED: PhD in plasma physics, high energy physics, nuclear engineering, or a closely related field; experience with Monte Carlo charged particle
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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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on estimated movements using eDCCs. The research will focus on data simulated using the Monte Carlo method and real data from clinical SPECT scanners with a parallel collimator, such as those available at LUMEN
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-resolution dosimeter, and new algorithms. Following this, the candidate will parameterize a Monte Carlo-based dose calculation system (e.g., GATE, TOPAS, or Geant4-based simulation tools) for evaluation in
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to the instrument development of SPHERES to meet future experimental challenges Develop novel polarization analysis components for the backscattering spectrometer by performing Monte-Carlo simulations and neutron