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Field
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highly challenging task. The project uses Machine Learning (ML), in combination with DFT and state-of-the-art Boltzmann transport methods, to predict, accelerate, and scale the computation of electronic
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Vacancies PhD position on indirect combustion noise; Fluid Mechanics; CFD; Aeroacoustics Key takeaways Simulation-based development of indirect combustion-noise theory To remedy ineluctable
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simulation techniques, including density functional theory (DFT), molecular dynamics, Monte Carlo methods, and free‑energy perturbation calculations. Develop and implement novel computational methodologies and
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credits). Practical experience of experimental spectroscopy or diffraction techniques such as XAS, XRD, FTIR, and XPS, of molecular modeling (MD/MC, DFT), and familiarity with basic programming (Python
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planning and learning from interaction. Possible directions include learning from video, demonstrations and simulation, and transferring knowledge across robot configurations. Adaptation and edge
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modelling, field experimentation, and hydrothermal analysis. Numerical simulations will be carried out using FEFLOW (or COMSOL/alternative) to represent coupled heat, flow, and transport processes in
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Validate the developed models against circuit simulations and measurements Perform hardware-algorithm co-design for AI-driven 6G transmitters Publish and present your results at leading international venues
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program by experimentally examining human behaviors of environmental relevance. To this end, it will make use of laboratory procedures (e.g., decision-making tasks, bicycle simulators) and consequential
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; • mathematical modelling, development of FEM models (preferably using Ansys), and simulations, particularly in the areas of electromagnetics, power losses, etc.; evaluation, verification, and optimization
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of the art in optical transceivers Understanding modeling of the optical modulators and simulation platform for simulation optical transceivers Develop high-bandwidth TX driver architectures for advanced