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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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in 2010, the OPDC cohort follows up 1700 individuals with PD, prodromal PD and control participants longitudinally, both at home and in clinic. Omics data collected includes genetics, metabolomics
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research projects involving large administrative claims, EHR, survey, or clinical trial data. Collaborate with external collaborators in implementing machine learning algorithms. Perform other duties as
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, including fluent data programming skills and an understanding of numerical algorithms used in data modeling and analysis. CSCAR @ ISR consultants must be capable of managing multiple projects and
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bootstrap inference for large datasets, variable-length time intervals and vectorised algorithms — and implement it in well-engineered, open-source R and Python packages (including SEQTaRget and pySEQTarget
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chromatography/mass spectrometry (LC-MS) measurements using principal component analysis, partial least squares, genetic algorithms, and other multivariate statistics. Current projects have accumulated a
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research projects involving large administrative claims, EHR, survey, or clinical trial data. Collaborate with external collaborators in implementing machine learning algorithms. Perform other duties as