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differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic Qualifications Candidates are required to have a doctorate or terminal degree in Computer
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of Computation Group, seeks applicants for a postdoctoral fellowship to conduct research in differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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Details Title HMS - Postdoctoral Fellow in Biomedical Informatics School Harvard Medical School Department/Area Biomedical Informatics Position Description Postdoctoral Fellows in Computational
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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flexibility, investment and planning under uncertainty, and the macroeconomic and policy implications of energy system transformation. Fellows will engage in rigorous empirical, computational, and theoretical
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system transformation. Fellows will engage in rigorous empirical, computational, and theoretical research that integrates engineering models of power systems with modern econometric and economic analysis
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, generative AI, NLP, or algorithmic decision systems Ideal applicants will have a strong background in operations research, statistics, or computer sciences and the ability to work across disciplinary