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strategies to design pulse sequences for high-resolution solid-state NMR under magic-angle spinning and static DNP in close collaboration with two PhD students working on these topics. The efficiency
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100%, Zurich, fixed-term Thesolid-state NMR research group in the Departement of Chemistry and Applied Biosciences is looking for a PhD student on the topic of pulse-sequence optimization based
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collaborate with other PhD students working on muscle technology, manipulation, control, and machine learning. The output of the PhD thesis will significantly contribute to the advancement of musculoskeletal
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in the field of production engineering. A further research focus is on novel topics such as additive manufacturing, Artificial Intelligence (AI), Machine Learning (ML), Big Data Analysis or Industry
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projects or employment). Your profile You have outstanding experience in Machine Learning with a PhD degree from a university in Computer Science, or related fields, with a proven track record in machine
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construction or related fields Knowledge and experience in data analysis and database management, as well as emerging technologies applied to the construction sector, such as BIM, scanning, machine learning
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are lookingfor outstanding, highly motivated individuals interested in pursuing a PhD in the multidisciplinary and important research area of modelling and optimisation of magnetic components and electric machines
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will be involved in developing a new service for circular information management for construction elements and reality capture in existing buildings using computer vision and machine learning. It
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from the machine learning domain to improve data analysis You will be responsible for project meetings, reporting, scientific publications, and conference/seminar presentations You will supervise master
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to exploit the large number of modes supported by the random structure to enable large-scale nonlinear optical operators for photonic machine learning and quantum optics. The former relies on the second