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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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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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-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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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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that polymers exhibit universal behavior for length scales larger than the local scale size of their monomer units. This has motivated the study of coarse-grained generic models, using Monte-Carlo and molecular
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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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data. Perform Monte Carlo simulation and experiments to further improve neutron instrumentation. Publish scientific papers resulting from this research and present results at appropriate national and
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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
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: variational formulation of neural network learning convergence of Langevin Monte Carlo algorithms Dissemination of research results through participation in scientific conferences, presentations, and
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eA Monte Carlo generators and detector simulations Experience with detector performance for high energy or nuclear physics experiments Experience with detector R&D and/or construction OTHER INFORMATION