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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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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