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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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machine learning techniques, and the ability to manage multiple projects. We recognize that finding a candidate with such a diverse background may not be possible, so we encourage anyone with experience in
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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. Qualifications Applicants at Postdoctoral Researcher level should hold a PhD in AI enabled learning, educational technology, information systems, computer supported learning, social entrepreneurship, innovation
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Qualifications • Highly recommended by scientists in the candidate’s field of study • Expertise in one of the following areas: Environmental or Performance Physiology, Machine Learning, Motion Analysis, Multiscale
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. B1 lit. b (postdoc) Limited until: 31.08.2032 Reference no.: 5718 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support
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. These workflows will then be applied in relevant Saudi Arabian contexts to help discover new ore deposits. The position will combine techniques from geological modelling, geostatistics, machine learning, and
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive
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physics, nuclear astrophysics, quantum sensing, machine learning, gravitational waves, and particle theory and experiment. Faculty members include Professors Weiping Liu, Jie Chen, Sebastian Garcia-Saenz
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical