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or manipulation principles Foundational understanding of advanced acoustic theories, including nonlinear, multimodal, or metamaterial-based phenomena Foundation in signal acquisition, data analysis, and imaging
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benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation-tools.php CBC Requirement It is the policy of The University of Texas at Arlington to conduct a
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, and nonlinear optics. We are developing efficient X-ray optical elements to build up the functionalities by controlling the 3D structure of materials over large volumes and on nanometre scales, such as
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National Institutes of Natural Sciences, National Institute for Physiological Sciences | Japan | 2 months ago
] (Upon hiring) In this call, we seek an early-career researcher dedicated to advancing laser microscopy toward higher sophistication and super-resolution using nonlinear optics or promoting high-definition
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analysis, machine learning, embedded DSP, digital design, logic synthesis, computer architecture, embedded systems, robotics, nonlinear and hybrid control systems, intelligent transportation systems
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impairments such as phase noise, I-Q imbalance and amplifier nonlinearities [9], that need proper compensation. Specific issues will be addressed in this project. Firstly, beam alignment between the transmitter
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and dynamical mean-field-theory levels, metal-insulator and structural phase transitions, quantum critical points, nonlinear and anomalous responses, optical traps, quantum computation and information
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language in the top menu). NON-MANDATORY SKILLS WILL BE POSITIVELY CONSIDERED Have experience in the modelling of the relation between environmental exposures and human health, e.g. distributed lag nonlinear
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required: nonlinear optics (e.g. optical parametric amplifiers), electrical switching, high-frequency electronics, thermal transport physics, and beamline experiments at user facilities. Besides technical
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned