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The Faculty of Engineering, Department Applied Physics and Photonics, is looking for a PhD-student with a doctoral grant. More concretely your work package, for the preparation of a doctorate
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samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive
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aims to develop hybrid quantum–classical approaches for modeling multiphase flows governed by complex, nonlinear dynamics across multiple scales. The postdoctoral researcher will investigate how
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of thick and strongly scattering samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize
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improvements. Examples include optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques
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, 100% funded PhD student position to fill starting around June 2026. Research is to be in the field of computational methods in nonlinear and large scale optimization / inverse problems or in novel
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ecosystem. Key Responsibilities Develop and optimize lithographic patterning of nano- and meso-scale structures, such as gratings, waveguides, cavities, and metamaterials for quantum and THz devices Integrate
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particular, we will use topology and shape optimisation methods to compute the optimal domain shapes that can stabilise solutions with desired/prescribed properties. We will use methods from inverse problems
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for representing a large class of nonlinear systems by a structure close to the linear case or defined by a set of linear submodels [Bernal, 2023, Lendek, 2010]. This representation facilitates the performance
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recommend that you apply early as the advert may be removed before the deadline. This fully funded PhD position, offers an exciting opportunity to develop and optimize multiscale models for surfactant