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interest in algorithms, blockchains and cryptocurrency, causal inference, game theory, learning, machine learning, market design, and networks, but all subjects at the intersection of these three scientific
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to facilitate timely and professional deliverables Experience with causal inference, machine learning, and artificial intelligence is desirable Organization: Yale School of Public Health Department: Biostatistics
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: The successful candidates are expected to work on the development and application of bioinformatic (integrative multi-omic methods) and/or computational mass spectrometry workflows (machine- and deep-learning
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or more of the following is desired: machine learning methods, statistical genetics, large-scale genomic analyses, cloud computing, and single-cell and or bulk sequencing analysis. The ideal candidate will
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, iNaturalist or other crowd sourced data, participatory mapping, maximum entropy modelling, machine learning are also a bonus. The position responsibilities include tasks related to research topics above, co