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machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes thus
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that combines modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks
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engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum
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-inspired solutions to address fundamental limitations of the current manifestations of the technology in terms of data stability and dynamic data operability in vitro and in vivo. You will be joining our
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are conventional and unconventional topological phases of matter, and quantum error correction codes and other quantum computing related topics. We invite theorists with backgrounds in condensed matter physics
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communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL - Quantum Reinforcement
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algebraic curves theory (and to construct the most suitable algebraic curves) that would give the best error-correcting codes, generalizing and eventually classifying the existing LRC codes. Your role in