-
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
-
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
-
Challenge: Generating realistic bathymetric maps at a large scale using satellite images and advanced machine learning methods. Change: Incorporating physics into satellite-derived bathymetry
-
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
-
, 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
-
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
-
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
-
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
-
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
-
) on the other hand. We use tools from statistical physics, information theory and non-linear dynamics to understand the how well a particular system responds to a stimulus, and how this stimulus is processed