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: Quantum simulation with Rydberg atom arrays (theory) Research Area: Atomic, molecular, and optical physics (AMO), quantum control, quantum simulation Relevant Fields: Quantum information science, quantum
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The purpose of the Mathematical Sciences Postdoctoral Research Fellowships (MSPRF) is to support future leaders in mathematics and statistics by facilitating their participation in postdoctoral
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For 2026-27, SLMath will appoint 6-8 Postdoctoral Fellowships to each of the programs. Fellowship applications are reviewed by program organizers and the Broadening Participation Advisory Committee, who recommend finalists to the Scientific Advisory Committee (SAC) for approval at its January...
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University of Northern British Columbia | Prince George North, British Columbia | Canada | 1 day ago
The Department of Mathematics and Statistics at the University of Northern British Columbia invites applications for two postdoctoral positions. These positions are open for researchers with
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Position in Number Theory and Representation Theory Department of Mathematical and Statistical Sciences University of Alberta Commencing Fall 2025 The Department of Mathematical and Statistical Sciences
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, applied mathematics, statistics, computer science, strong gravity, condensed matter theory, particle physics, quantum fields & strings, quantum gravity, quantum foundations, quantum Appl Deadline: 2026/01
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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. The projects may also include to tackle benchmarking problems such as SAT, image processing, graph theories, boson/fermion sampling by applying classical machine/deep learning, neural network techniques and
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position focuses on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and
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on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and reinforcement learning