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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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performance, we will pioneer novel physics-informed machine learning algorithms to formulate an optical link performance map. This groundbreaking project comprises two PhD positions and one postdoc position
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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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than two spherical daughter vesicles, even though there may still be an energy barrier involved in the actual splitting process. To achieve this goal, we will use a combination of analytical and
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additive manufacturing. We will work with CEAD who have developed an additive manufacturing process combined with fibre reinforcement to drastically enhance the properties of very large printed structures
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. Master's degree in Earth sciences, physics, applied mathematics, engineering, computer science or related disciplines. A strong background in either: ) fluid dynamics and numerical modelling ) satellite