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Field
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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, engineering, or a related field. Strong programming skills and experience in machine learning or statistical modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning
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quantum dots. In particular, based on our recent result in small arrays, we aim at exploiting coherent control of spin qubit in larger arrays to perform quantum simulation or algorithm. Therefore
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
optimization algorithms Extensive communications protocol design Solid understanding of physical/link layer communications, including synchronization, beamforming, and autonomous rate adaptation Point of Contact
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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seeking to recruit one post-doctoral researcher. Job Description The candidate will participate in the R&D activities of FORTH-IESL within the COLOURS project, working on the development of algorithms and
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, high-performance computing (HPC), and computational sciences. Major Duties/Responsibilities: Participate in: (1) design and implementation of scalable DL algorithms for atomistic materials modeling
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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on are: • approximation algorithms in QIT (e.g. ground state energies); • symmetry- and size reduction of polynomial optimization problems; • characterization of quantum codes and quantum capacities
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis