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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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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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contributing towards a new approach to fusion bonding, combining process and performance optimization by simulations of physical phenomena and process monitoring using artificial intelligence. This postdoc will
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post-processing technology. Your key responsibilities include: Supporting the image sensor team with mask design, device physics simulations and other analytical modelling Assistance with the clean room
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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
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phenomena in a tunable fashion, to simulate non-trivial quantum physics, and ultimately to compute classically hard problems. A foundational aspect to make impact and to enable advances in this domain is the
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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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through prior work and/or education. Experience with process-oriented working approach is an advantage Persuasive with sensitivity: using the right arguments and examples for the specific stakeholders
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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