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, the world-leading research and innovation hub in nano-electronics and digital technologies. Our team is currently a fruitful mixture of people from different nationalities. The primary working language is
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and adapt assimilation schemes based on generative deep learning methods (such as flow matching and diffusion models). The candidate should have previous experience in data assimilation and/or deep
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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. The aim of the current project is to further identify when, for whom, and under which conditions the intervention is most effective, using both quantitative findings (including Latent Change Modelling) and
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on-chip solid-state lasers Modelling and design of PIC narrow linewidth CW lasers for trapped ion/cold atom/color center quantum control Modelling and design of PIC femtosecond lasers for frequency
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/ dry-etch optimization / thin-film development) Testing and characterizing on-chip solid-state lasers Modelling and design of PIC narrow linewidth CW lasers for trapped ion/cold atom/color center quantum
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background in nanomagnetism, spintronics, or magnetic materials Experience with NV microscopy or other advanced magnetic imaging techniques Background in micromagnetic simulations or numerical modelling