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or interest in the use of artificial intelligence, machine learning, or computational tools in behavioral and experimental economics is appreciated. Strong emphasis will be placed on demonstrated research
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
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(https://www.hsph.harvard.edu/lin-lab/ ), Professor of Biostatistics and Professor of Statistics. The postdoctoral fellow will develop and apply statistical, machine learning (ML), and AI methods
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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 the importance
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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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, machine learning, statistics and programming skills (R and Python) is preferred. Record of peer-reviewed publications. Knowledge in one or more of the following areas is desirable: single-cell profiling
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biophysics -experimental and/or computational genomics -computer science, statistics, and/or machine learning with applications relevant to genomics -bioinformatics -population genetics / genomics
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to the Computing Sciences Area and Berkeley Lab. Alvarez Fellows apply advances in computer science, mathematics, computational science, data science, machine learning or AI to computational modeling, simulations
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational