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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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conferences (including presenting your research results) and preparation of scientific publications with Peer-Review Your Profile: Completed university masters degree (physics/chemistry) with a PhD Strong
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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magnetic micro-actuator physics and practical skills for MEMS device engineering. Your Profile: You are studying physics, chemistry, materials science, nanoscience, nanotechnology or a related subject at a
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hydrogen storage process. Establishment of operando scanning probe microscopy setup for interface analysis of chemical hydrogen storage process Design of operando (electro)chemical reactors for multi-modal
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the initial stage, you will synthesise ring polymers using anionic polymerisation techniques. You will also develop new linking chemistries and purification processes. The physics part of the project will focus
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to make them usable for transparent energy system analyses. You will process and semantically enrich the collected data before it is integrated into a knowledge-graph-based metadata platform. In
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coding in C++, supported by specialised training (e.g. C++ courses). Your Profile Master's degree in Mathematics, Computer Science, Physics or a related discipline Object-oriented programming skills
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problems in materials science, solid-state physics and chemistry, biology and soft matter. We carry out systematic characterizations using various complementary techniques, including the latest TEM based