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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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maintain existing analysis/sorting equipment and computer systems. Participate in the selection of new facility equipment and software with advancing technology so that the facility maintains its status as a
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competences of the faculty and research team members at the ETH Zurich in Switzerland ideally complement the competences in natural sciences and computer science at the Max Planck Institute for Intelligent
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, computer science, or a closely related discipline (typically first-class or high 2:1, or equivalent; Master’s welcome) • Strong programming skills (for example Python, MATLAB, C/C++) • Strength in at least two of
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: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science, Gaming Engineering or a related discipline. · Knowledge: Strong coding skills in Python and knowledge in materials
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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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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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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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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