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Field
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will
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"Phase-space-inspired numerical methods for high-frequency wave scattering: from semiclassical analysis through numerical analysis to implementation". The design of fast and reliable algorithms
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theoretical foundations of modern learning algorithms. The position will be supervised by Florence d’Alché-Buc, Charlotte Laclau, and with Rémi Flamary and Karim Lounici from École Polytechnique
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areas: + Quantum computing and quantum algorithms + Solid-state physics, computational materials science, or quantum chemistry + Battery materials modeling Excellent programming skills (e.g., Python) and
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, telecommunications or related field. Other requirements include Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design
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characterization and benchmarking techniques for quantum algorithms on devices at or beyond the 16-transmon scale. They will also contribute to designing large-scale control and readout multiplexing based on cQED
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quantum algorithms for quantum many-body physics. A PhD in Physics is required. The ideal candidate will have numerical simulation skills with exact diagonalization/quantum-circuit techniques and/or
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and paleosols 3) train and test deep learning algorithms. You will be required to take responsibility for all the steps involved in the “Phytolith analysis” work package of DEMODRIVERS. This will
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. Experience with phase retrieval algorithms, clean room use and e-beam lithography are beneficial. The candidate will be expected to participate at international user facilities and thus will be expected
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evaluation of algorithms for early drought stress detection by integrating interactive manipulation strategies with learning-based monitoring methods. This includes designing interaction primitives, processing