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role in advancing our mission to develop next-generation digital twin frameworks that integrate physics-based modeling, data-driven inference, and real-time monitoring to support robust lifecycle
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the investigation of confidential computing, in support of distributed machine learning and inference. A successful candidate will have a general understanding of distributed computing, trusted execution environment
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. The analyst partners with researchers, bioinformaticians, and data scientists to provision elastic, fault-tolerant systems for genomic pipelines, AI/ML model inference, and large-scale data lake analytics
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analytical techniques for causal inference and prediction, and writing papers for both an academic audience and for practitioners (managers and/or policymakers). Desired Qualifications: Experience working with
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, Biomedical/Health Informatics or Computer Science. Strong quantitative background in pharmacoepidemiologic methods, bioinformatics, causal inference modeling, AI/ML methods. Prior experience with analyzing
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, perform cutting-edge analytical techniques for causal inference and prediction, and writing papers for both an academic audience and for practitioners (managers and/or policymakers). Desired Qualifications
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Services Research, Statistics/Biostatistics, Biomedical/Health Informatics or Computer Science. Strong quantitative background in pharmacoepidemiologic methods, bioinformatics, causal inference modeling, AI
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that are applicable to GRC’s work are survey sampling and methodology, small area estimation, statistical process control, probabilistic linkage, causal inference methods, and predictive modelling. GRC’s current
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matter expertise in survey design and estimation, statistical disclosure avoidance, and causal inference; (7) contribute to new project development; (8) develop and implement research policies and
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knowledge of regression modeling with generalized linear mixed models; experience designing and using survey weights; a strong foundation on the requirements for causal inference; accurate and effective