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Mission Connecting urban systems, human behaviour, and well-being: How can cities reduce resource use and carbon emissions while maintaining or improving human well-being? How do changes in urban
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participation in the annual team meetings held in the case-study cities. Access to the computing infrastructure and to the resources of EPFL, including the Excellence in Africa Center (EXAF). A central
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EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than
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communications, computing, metrology, sensing or ranging. Yet, it relies on the availability of broadband tunable, narrow linewidth, and bright microwave sources that can be used to excite a high
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nonlinear imaging techniques to study, characterize and image water in different configuration. Introduction Electrolyte solutions in water are an essential ingredient of many aspects of life and technology
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Mission Understanding how urban sustainability transitions interact: How do transformations in different urban sectors—such as energy and food—interact? Under what conditions do they reinforce one
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PhD Student Position in Interoperable Digital Workflows for Regenerative Building Design and Perform
Mission The South–North Laboratory for Sustainable Construction and Conservation (SONO Lab) at EPFL is recruiting a full-time PhD Student (100%) within the School of Architecture, Civil and
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modelling satellite imagery and textual conflict data. Advances in vision-language representation learning will be explored to link the two sources of information through alignment pipelines, localisation
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regular faculty, and gradually progresses to the latter. Instructors are expected to be able to teach courses in English or French. Applicants should have a PhD in Mathematics or in Computer Science
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background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn generative deep learning – in