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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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, probabalistic algorithms Noam Lifshitz Extremal combinatorics, additive combinatorics, analysis of boolean functions Elon Lindenstrauss Homogeneous dynamics, ergodic theory, number theory, additive
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(Numerical Analysis and Applied Mathematics) develops numerical algorithms and software for large-scale problems in science and engineering. Its research ranges from algorithm design and analysis to software
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transcriptomic technologies and biological data interpretation is a plus. Familiarity with optimizing cell segmentation algorithms for enhanced accuracy and efficiency, as well as experience with tools/packages
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development. * Implementing computationally intensive algorithms on high-performance computational clusters. * Developing AI/ML-based causal inference models for real-world evidence applications (e.g., target
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algorithmic consequences. Topics of interest include coarse equivalents of classical graph theorems and parameters, asymptotic minors, and coarse embeddings; Where to apply E-mail job-ref-5sp0nv8qvb
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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asap, focused on ‘Quantum Machine Learning’, with the objective of investigating hybrid classical-quantum and quantum inspired algorithms. The tasks will include the design and implementation
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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