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Field
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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Pharmacoepidemiology and Pain/Opioid Outcomes Research. This two-year, mentored training program provides intensive, hands-on experience in real-world data analysis, causal inference, and comparative effectiveness
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coarse-grained models that can be analyzed and simulated. Strong applicants with backgrounds in applied and computational mathematics, biophysics, engineering, statistical inference, and related fields
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current and next-generation galaxy and time-domain surveys. You will work closely with our teams on the Rubin Observatory ‘s LSST, Euclid, ZTF, and LS4. Main responsibilities Field-level inference and
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or ephemeral services supporting workflows, such as databases, workflow engines, cloud-native frameworks, AI inference front ends, and REST APIs. Coordinating dynamic service deployments and specifying storage
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: Development, performance analysis and optimization of end-to-end science workflows, including those originating at DOE facilities. Deployment of capabilities such as AI training and inference at scale, and
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and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology
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& Other Requirements Demonstrated abilities in mathematical modeling, analysis and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis
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, analysis and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology
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, ideally including multilevel modelling, experience with reweighting techniques, and preferably expertise in Bayesian data analysis. Ideally the post-holder would be able to start the post before 1 October