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machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
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The Doctoral Candidate will: Perform numerical modelling of the three NDE techniques to evaluate the influence of relevant material property gradients on each NDE observable generating a sizable synthetic
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numerical simulations on NYUAD’s High-Performance Computing (HPC) system. Support preparation of scientific manuscripts and presentations: Assist in drafting reports, papers, and conference materials
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of ultracold atoms. - Develop and expand collaborations with theoretical groups and well development of numerical toolbox to simulate magnetic atom is optical lattices in line with strategic objectives. - Mentor
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address key challenges within these processes, constructing robust models and simulations that deepen the understanding of the underlying physics involved. The ultimate goal is to create predictive, physics
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theoretical models and numerical tools (master equations, quantum trajectory simulations) to investigate coupling regimes, dynamical phase transitions, and the effects of collective dissipation on coherence and
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, numerical methods, or Geant4 / Monte Carlo simulations. Proven experience in scientific software development using C/C++, Python, MATLAB, CUDA, and/or other relevant programming tools. Demonstrated ability
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using gem5 simulator as the main execution backend for the RL agent, while also investigating the potential of RTL simulation and physical RISC-V board as complementary execution nvironments. A key aspect
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(CFD), surrogate modelling, and reduced-order modelling (ROM) for thermal energy systems. As a Research Engineer, you will be responsible for developing and executing steady-state CFD simulations
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of mathematical derivations — checking proofs, running numerical simulations, implementing models in Python or Mathematica, researching the relevant literature. You do not need to be an expert in all areas the QBF