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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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Challenge: Generating realistic bathymetric maps at a large scale using satellite images and advanced machine learning methods. Change: Incorporating physics into satellite-derived bathymetry
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bioprocesses still have. This effort includes quantifying the impact of various process routes as well as creating micro-organisms (or consortia thereof) that can deal with new feed stocks or produce more
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, urban planning, building technology, social sciences, process management, and geo-information science. The faculty works closely with other faculties, universities, private parties, and the public sector
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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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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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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
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the state of the art, one can rely on physics prior and latest AI techniques to accelerate ULM imaging in 3 dimensions. In this PhD project you will be embedded in a multidisciplinary collaboration
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that the manual process is very time-consuming and error-prone. To help drive efficiency, computer vision is crucial, yet so far the focus has been on single-camera data analysis. As a PhD at TU Delft you will