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computer science, statistics, operations research, or related computational fields. As part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel
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models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees. Research areas include Representation Learning, Machine learning and Optimization
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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
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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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design and reinforcement learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools
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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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neighborhoods to define using cellular protein expression, then implement appropriate computational approaches to define these neighborhoods (e.g., Latent-Dirichlet Algorithm, network analysis, etc.). Work with
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tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact. Complete other responsibilities as assigned. Qualifications Basic Qualifications: Minimum of seven
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problems that no other institution can match. When you work at Harvard Kennedy School, you make a difference. Job Description Professor Goel’s Computational Policy Lab at the Shorenstein Center at Harvard