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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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Develop collaborative relationships with faculty and researchers across Harvard schools and engage substantively with research communities at the Kempner Institute for the Study of Natural and Artificial
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is interested in understanding the neural and algorithmic basis of sensory-guided behaviors in terrestrial animals. We have developed behavioral tasks in mice using stimuli and situations
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: Experience with developing efficient numerical algorithms and modifying electronic structure DFT or quantum chemistry software (e.g. Quantum Espresso, PySCF, GPAW), fluency with electronic structure theory and
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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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, developing scalable algorithms for system optimization and control, conducting policy-relevant economic analysis, programming following best practices for reproducible research, presenting findings to academic
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challenge meeting this requirement is the simultaneous need for low-power consumption. The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise
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. Responsibilities include conceptualizing and implementing statistical and structural models, developing scalable algorithms for system optimization and control, conducting policy-relevant economic analysis
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variety of cutting-edge research in time-domain astrophysics, including the development and implementation of machine learning, statistical and data-driven algorithms to study exotic transient phenomena