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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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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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in AI-modeling, Monte Carlo simulation, and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize
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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
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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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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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interactions), (vi) Uncertainty analysis (Monte Carlo simulations, surrogate modeling), (vii) Risk analysis. The position contributes to the ANR project IM-SURF (Impact and Mitigation of SURFace Seismic Waves
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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