24 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" Fellowship positions at Harvard University in United States
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machine learning. The specific goal is to extend new and existing visualization environments to support efficient and precise annotation of histopathology images using a combination of expert human review
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simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on the importance of design and innovation
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rapid technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on
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/HCI: PhD in Computer Science, Human-Computer Interaction, Information Science, or related computational fields with expertise in machine learning, natural language processing, human-AI interaction
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu/ . For questions, please email mweber@seas.harvard.edu Applications will be reviewed on a
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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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world are facing dramatic upheaval as a result of rapid technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic
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, or Stata); · Creating and managing very large datasets; · Machine learning skills. Basic Qualifications A Ph.D. in any business discipline, organizational behavior, economics, statistics, environmental
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of References Allowed Keywords statistics, biostatistics, computer science, economics, health care policy, causal inference, machine learning