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Field
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collection methods, including development of in-person data collection tools (such as surveys and interview schedules) and familiarity with or experience in secondary data and its analysis. Demonstrated
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at all levels; offer vital services to scientists in the public and private sectors within the member states; develop new instruments and methods; and engage actively in technology transfer
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background and strong analytical skills in quantum physics and applied mathematics, including familiarity with exact and approximate methods to solve for and control the time-evolution of quantum many-body
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Fellowship period at the University of Oslo. Job description The person hired in the position will work on theoretical control and optimization methods for a grid island powered by renewable energy, supporting
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Responsibilities: Development of stochastic and analytical methods for nonlinear partial differential equations Implementation of relevant numerical experiments using deep learning algorithms Job Requirements: PhD
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quantum phenomena in strongly correlated systems; Analytic or numerical methods of quantum many-body systems and non-equilibrium systems; Quantum field theory; Modeling and calculation of solid-state
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that range in scale from atoms to continents and apply methods where fieldwork, numerical modelling, experiments and theory act in concert. The center includes the Oslo-branch of PoreLab, which is a Center of
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processes as droplets/condensates wet membrane compartments in cells. Numerical simulations and theoretical membrane models will be developed, aiming to couple viscous interfacial fluid flow, elastic
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the Research Council of Norway. In this project, we will use advanced time-lapse imaging, numerical simulations, and reactive mixing theories to better understand and predict the role of fluid mixing as a driver
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methods for a grid island powered by renewable energy, supporting both the ATLAST telescope and the nearby community of San Pedro de Atacama. The PhD candidate will have the unique opportunity to shape