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, a combination of theory, numerical modeling and the application of machine learning for reduced physics and surrogate models are used. The research will be performed in an international environment
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tackling these theoretical challenges, you will be able to interact closely with experimental colleagues. Information about the division and the department The position will be placed in the theory division
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Is the Job related to staff position within a Research Infrastructure? No Offer Description We are offering a PhD position in the field of algorithmic graph theory. The position is a full-time
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foundations with a research base in experiment and theory providing a thriving and internationally leading research environment in the field of quantum technologies. Read more for details about the research
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guided vehicles (particularly self-driving cars), image-based localization, structure-from-motion, and object recognition. The main research problems include mathematical theory, algorithms, and machine
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, and object recognition. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as well as application
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student will be supervised by Moa Johansson in the Data Science and AI division , and co-supervised Thierry Coquand in the Type Theory group in the Computing Science division . Johansson's group focuses
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description The goal of this PhD project is to develop techniques for the design and verification of assured ACPS with a focus on runtime assurance. You will develop theory and tools for the construction
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mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as well as application to medical problems. Major responsibilities Your major
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, communication skills and a passion to combine theory and practice. Additional merits that are considered: - Experience in interdisciplinary cooperation, as well as experience in collaborations with partners