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safety, innovation, and making a tangible impact, we encourage your application. Please note that the successful applicant will also need to satisfy the general requirements of entry into a PhD program at
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. The research program may also involve a numerical simulation component. Your tasks #analyzing measurements of ocean turbulence using autonomous glider vehicles #use and develop machine learning methods
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Completed university studies (Master/Diploma) in the field of mechanical engineering, physics of related disciplines Enjoy experimental work in international and multidisciplinary teams Communicative
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“Light- versus electron-induced spin-state switching of complexes on insulating layers” within the Priority Programme SPP 2491 “Interactive Spin-State Switching” This DFG-funded project aims
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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experimental mechanics, classical materials testing also benefits from a range of well-established methods. However, biomedical applications lack such a wealth of standardized protocols to address the diverse
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a closely related field. A strong background in quantum mechanics, solid-state physics, and computational modeling. Previous experience with density functional theory or many-body physics (beneficial
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to identify novel biomarkers and gain a better understanding of mechanisms underlying these inflammatory diseases. All candidates should have a strong interest in population or clinical research, be able
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1st class degree in Engineering with strong background in theoretical and computational mechanics of solids/materials. To apply please contact the supervisor, Prof Andrey Jivkov - andrey.jivkov
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of the mechanical properties of the cellular actin cortex, the biomechanics of cell division, and the coupling between cell shape and mechanics and cellular state / fate during cellular transitions. The successful