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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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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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research fellow driven to advance the safety, sustainability and reliability of nuclear energy. You will develop theoretical knowledge of nuclear reactor physics and apply this in modern numerical methods
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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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A leader who brings people together in creating a strategic vision and shape the future of our research department. That’s the challenging role you will have as Head of Department Imaging Physics
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the Innovation for Delft Engineering Education (IDEE) initiative, on the theme “Students taking responsibility for their own learning process”. For this post-doc position, you will investigate how learning in
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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