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relevant. The steps in the development of surrogate models are building data-driven models from medical imaging, extending them with physics-based approaches, and adapting existing physics-integrated neural
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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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-computing hardware Work in an interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state
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engineering, biotechnology, computational biophysics, bioinformatics, data science, or a closely related discipline with a strong academic record Genuine interest in data-driven and physics-based modeling
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, computer science, physics, material science, earth science, life science, engineering, or a related field Proficiency in at least one programming language (Python, R, C++, Julia, …) Good analytical skills with a
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physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic environments Design and implement reinforcement learning algorithms for control and
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are researching this and similar questions. Our goal is to systematically optimise the interaction between people, organisations and technology. With us, your theory also passes the practical test. Bring your
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requirements, see below): Jordan University of Science and Technology, Irbid (JUST) M.Sc. Computer Engineering* M.Sc. Network Engineering and Security* M.Sc. Radiologic Technology** M.Sc. Medical Laboratory
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the fields of medicine, biology, chemistry, physics, technology, and the humanities. Since 1948, MPI researchers have been awarded 21 Nobel prizes, which testifies to the quality and innovation of MPI research