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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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on account of that individual’s sex; sexual orientation; gender identity or expression; pregnancy, childbirth or related conditions; race; color; national or ethnic origin; religion; age; disability; protected
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skills; access to social-emotional learning in school; and the role of emotionally supportive adult caregivers (parents, educators, coaches, and mentors). The postdoctoral associate’s primary
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methylation sequencing, single cell DNA methylation, and single cell transcriptome • Perform machine learning/deep learning analyses; and delivering analytical results. • Develop workflows for multiple
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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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, genetic and neuroimaging data to identify new biological mechanisms and develop precision medicine tools for these conditions. We have a track record of training outstanding postdocs who go on to successful
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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