Computational analysis of large, complex, single cell genomics datasets from in vitro cellular model systems ; Deliver testable hypotheses/insights from complex high-dimensional data to inform target selection ; Linking results and insights between internal and public data, as well as orthogonal data such as human genetics. ; Identify and validate approaches to improve quality and efficiency of hypothesis generation from model systems ; Maintain awareness of emerging methods in computational biology and applications for novel omics technologies ; Provide ad-hoc bioinformatics support to cross-disciplinary project teams
Requirements:
PhD in Bioinformatics, Biostatistics, Computer Science, Computational Biology, Genetics, Mathematics, Physics, Statistics or other related discipline ; 2-3 years post PhD experience applying quantitative approaches to solve biological problems ; Knowledge and experience of single-cell transcriptomic data and analyses; additional modalities such as histone modification data analysis considered a plus. ; Strong knowledge of applied statistics and machine learning (in particular deep generative models) ; Strong statistical and scripting programming skills (Python/R/etc.)
Text:
Computational Biologist Computational analysis of large, complex, single cell genomics datasets from in vitro cellular model systems ; Deliver testable hypotheses/insights from complex high-dimensional data to inform target selection ; Linking results and insights between internal and public data, as well as orthogonal data such as human genetics. ; Identify and validate approaches to improve quality and efficiency of hypothesis generation from model systems ; Maintain awareness of emerging methods in computational biology and applications for novel omics technologies ; Provide ad-hoc bioinformatics support to cross-disciplinary project teams PhD in Bioinformatics, Biostatistics, Computer Science, Computational Biology, Genetics, Mathematics, Physics, Statistics or other related discipline ; 2-3 years post PhD experience applying quantitative approaches to solve biological problems ; Knowledge and experience of single-cell transcriptomic data and analyses; additional modalities such as histone modification data analysis considered a plus. ; Strong knowledge of applied statistics and machine learning (in particular deep generative models) ; Strong statistical and scripting programming skills (Python/R/etc.)
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