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to compare the genetic architecture of rare vs. common genetic variation 2. Developing methods to analyze GWAS data using graphical models and genome-wide genealogies 3. Developing methods to integrate rare
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of Engineering and Applied Sciences. The fellow will design and run human experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be
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of intelligence, including mathematical and computational models of intelligence, cognitive theories of intelligence, and the neurobiological basis of intelligence. Research and development of new AI or ML
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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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in the Fontana lab at Harvard Medical School in Boston Massachusetts. The work of the Fontana lab is focused on developing and applying software for modeling combinatorial systems of molecular
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differences more constructively. Fellows contribute to creating pedagogical resources, designing tools for dialogue, and testing models that help make engagement more inclusive and meaningful while also working
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quasi-experimental methods to identify causal effects and test the predictions of economic and sociological models. Examples of current research projects include: long-term impacts of neighborhoods and
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expertise in at least one (or more) of the following areas: Computer Architecture/Systems: Design, modeling, simulation, and/or physical design of heterogeneous system/processor architectures; Silicon
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differences more constructively. Fellows contribute to creating pedagogical resources, designing tools for dialogue, and testing models that help make engagement more inclusive and meaningful while also working
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death, cancer, toxicology, environmental exposures), Zhi-Min Yuan (genetic engineered mouse models, p53, lung fibrosis). Together, we investigate mechanisms of disease that arise from environmental