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Join us on our quest to overcome a long-standing research challenge in soft tissue biomechanics through the combination of multi-modal experimental tissue testing data, machine learning and physics
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future is a thorough understanding and assessment of the thermo-physical properties (e.g melting temperature, heat capacity, density, viscosity, thermal conductivity) of the molten fuel salt during reactor
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simulation models and enriched by operational equipment performance data. To this end, physics informed machine learning techniques will be used to bring model data and real data together in a Digital Twin
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with companies in this area. We are looking for a candidate with a strong background in physics or in a physics related discipline. In particular, we want the candidate to have expertise in a branch of
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contributing towards a new approach to fusion bonding, combining process and performance optimization by simulations of physical phenomena and process monitoring using artificial intelligence. This postdoc will
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post-processing technology. Your key responsibilities include: Supporting the image sensor team with mask design, device physics simulations and other analytical modelling Assistance with the clean room
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complex fiber networks in the brain and other biological tissues. The position is in the group of Dr. Miriam Menzel, at the Department of Imaging Physics. The group works on scattered light microscopy
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the Department of Imaging Physics (www.imphys.tudelft.nl ) of the Faculty of Applied Sciences. This department performs cuttingedge research spanning the range from understanding the basic principles underlying
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physical healthcare spaces, how can architecture help mediate and enhance the ethical dimensions of care, particularly concerning patient autonomy and consent in an increasingly monitored environment
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topologies (sampling problem) and angular estimation algorithms (estimation problem) is required, while considering the physical effects (EM problem). The novel idea in DoAnt is to develop an EM-driven