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, relevant, and scientifically valid evidence to improve health policy and practice. IHME carries out its mission through a range of projects within different research areas including the Global Burden
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within different research areas including the Global Burden of Diseases (GBD), Injuries, and Risk Factors; Future Health Scenarios; Cost Effectiveness and Efficiency; Resource Tracking; and Impact
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within different research areas including the Global Burden of Diseases (GBD), Injuries, and Risk Factors; Future Health Scenarios; Cost Effectiveness and Efficiency; Resource Tracking; and Impact
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• Flexibility to learn new technologies, APIs, and SDKs by reading documentation • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc
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learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. • Experience with common data science toolkits, such as R, Weka, NumPy, MATLAB, etc. Excellence in at least one of
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, APIs, and SDKs by reading documentation • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. • Experience with common data
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either back-end Java development, front-end UI development, and/or signal processing algorithm development. This position will work on all phases of software application development ranging from
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computational tools, pipelines, or algorithms to improve the accuracy and speed of genomic workflows, particularly for rare variants and noncoding regions. • Functional Follow-up: Implementing functional assays