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department, with active theoretical and experimental research in condensed matter and AMO physics, as well as correlated condensed matter, quantum materials, driven/dissipative systems, and quantum information
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skills in English and a passion for contributing to sustainable nuclear materials research. LanguagesENGLISHLevelExcellent Internal Application form(s) needed 20231016-application-form-electronic
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students (including 250 foreign students) and 380 staff. The school has 3 research and teaching centres of a high scientific and technological level, working in the fields of materials and civil engineering
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for an interview, external Candidates will be required to bring a copy of their transcripts to the interview. The selected candidate will follow a standard course outline and assessment scheme. https://www.tru.ca
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simulator systems Task trainer systems during practice, rehearsal, testing, research and learner use Immersive patient care management simulation scenarios Skills & task training labs Executes high fidelity
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direct support to NASA’s Space Radiation Program. The selected candidate will conduct research focused on developing computer models to simulate the interaction of high charge (Z) and energy (HZE) ions
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state physics Technology » Nanotechnology Chemistry » Physical chemistry Physics » Applied physics Technology » Materials technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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). The documents required for applying A letter of application to the Rector. A curriculum vitae. A list of research publications (if necessary, enclosing copies or offprints of more important publications). A copy
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, Community and Technical College, Kasitsna Bay Campus, Seward Marine Center, Tok Campus, Kuskokwim Campus, and Northwest Campus) Online: https://www.uaf.edu/orca/files/ASFSR.pdf . Request a paper copy in
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materials according to the Lambert–Beer law, thus enabling an accurate description of PEC device behavior. In parallel, the coupling between kMC and CFD simulations will be achieved through machine learning