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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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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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Sustainable Process & Energy Technology The Department of Process and Energy (P&E) of Delft University of Technology (TU Delft), The Netherlands, announces an open position for an Assistant
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Kick off your career in the process industry (chemicals, energy, food, pharma) in Delft. As an Engineering Doctorate (EngD) trainee in Process and Equipment Design at the world-class institute TU
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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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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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) 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
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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