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Field
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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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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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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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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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and initiatives in the field; participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment; support the Φ
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
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using both physical and machine learning-based algorithms. Strong interpersonal and communication skills and the ability to work both independently and collaboratively with researchers and students from
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AI Algorithm and Autonomous Decision-Making Systems RESPONSIBILITIES Research and develop autonomous decision-making algorithms for process industries (e.g., chemical, pharmaceutical, materials
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postdoctoral associates to join this endowed center. The Schmidt Center’s mission is to create the theoretical, computational and algorithmic paradigms that will enable us to understand the programs of life and