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instruction is English. All courses, lectures, and workshops are held in English. Participants are required to write and defend their doctoral thesis in English. Programme duration 8 semesters Beginning Winter
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we are looking for The candidate should have a 1st or high 2:1 degree in mechanical/aerospace/manufacturing engineering, computer science, physics, mathematics, or related scientific disciplines
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interactions and nanophotonics, as well as to theoretical physicists in the Department of Mathematics and Computer Science. We collaborate closely with scientists of the ALPS II collaboration and parts
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One start-up scholarship (for 1 year) within the Department of Ancient Civilizations at the University of Basel The PhD program of the Department of Ancient Civilizations at the University of Basel
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My research focuses on strongly interacting quantum systems at the interface between condensed matter physics and ultracold atomic gases. In particular, I am interested in the interplay between few- and many-body physics in scenarios ranging from superfluids to quantum impurity problems to...
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I am interested in the most catastrophic and explosive collisions in the Universe, such as the mergers of neutron stars and black holes. I study these using both gravitational waves and electromagnetic signatures, primarily focussed on linking the data from these exciting experiments with our...
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will be enrolled in the electrical engineering doctoral program at ETH Zurich. The project is embedded in the National Competence Center for Research Muoniverse and funded by the Swiss National Science
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, informatics, physics or a related field strong expertise in machine learning strong interest in high performance computing on CPUs and GPUs proficiency in Fortran, Python, shell scripting proficiency with Linux
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through
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integrating machine learning, computational modelling, and experimental validation. The successful candidate will receive training in both computational and experimental biology within a highly collaborative