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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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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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of complex systems, networks, and large-scale data Machine learning, generative AI, NLP, or algorithmic decision systems Ideal applicants will have a strong background in operations research, statistics
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algorithms and best clinical judgment in making decisions regarding anticoagulant dosage adjustments and patient reevaluation. Considers and refers any patient appropriate for independent INR monitoring
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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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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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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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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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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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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