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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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period of up to 12 months in the first instance, with the possibility of an extension, subject to funding. The project entails the development of a kinetic Monte Carlo (KMC) framework for the simulation
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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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, renormalization group techniques or Monte-Carlo methods. Investigating topological properties of magnetic quantum states such as fractional quasiparticle excitations in spin liquids. Transferring the obtained
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
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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qualifications PhD or equivalent in Nuclear Engineering, Physics, Chemistry, Materials Science or related disciplines Demonstrated proficiency with computational modeling, e.g., DFT simulations, Monte Carlo
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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