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professional deliverables ● Experience with causal inference, machine learning, and artificial intelligence is desirable ● Experience with clinical, EHR, or biobank data analyses is desirable
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Forests pedagogical approach–allowing participants to engage in hands-on, place-based learning. About the-ELTI Rwanda Program As one of the first countries to commit to the Bonn Challenge, the Rwandan
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in candidates with interest in algorithms, blockchains and cryptocurrency, causal inference, game theory, learning, machine learning, market design, and networks, but all subjects at the intersection
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. Fluency in Japanese is an important advantage, but applications are also encouraged from candidates with complementary technical expertise and a strong interest in learning about Japanese history
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(SUDs), psychiatric conditions, and other behavioral and lifestyle characteristics that impact human health using large datasets and biobanks including the Million Veteran Program (MVP), the SUD working
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, survey testing, data quality monitoring, and more. Conduct econometric analysis under the direction of senior researchers. Manage relationships with collaborating institutions, including data collection
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strong background in machine learning and a keen interest in neurobiology. Previous experience in topics such as gene-regulatory networks, xQTL analysis (splicing, expression, chromatin accessibility
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specializing in modern Chinese history; at least half of the candidate’s dissertation must concern post-1911 China. The successful candidate will be expected to teach one or two courses depending on the needs
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independent research. The successful candidate will work in a team of fellow trainees in the lab. Learn more about the Tang lab at https://www.steventanglab.com. The Tang lab is committed to training
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scientists, etc), and an enthusiasm to understand and explore the underling biological processes. We think the best career opportunities for postdocs come in an environment where you lead major projects