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Position: Research Scientist I, Computational Genomics
Institution: Vertex Pharmaceuticals
Location: San Diego, California, United States
Duties: single-cell characterization (sc/snRNA-Seq); gene expression analysis (RNA-Seq, eQTL, Pathway/network analysis); bioinformatics engineering (programming, analytical pipeline development, data visualization, high-performance/cloud computing); statistical genetics (GWAS/PheWAS/Fine mapping/Gene burden testing); statistics and machine learning (traditional statistical analysis, decision theory, clustering, network analysis, text mining); evolutionary/population genetic sequence analysis (Population structure, Homology/Conservation inference)
Requirements: PhD or MS with experience in computational biology, statistical genetics, population genetics, bioinformatics, biomedical engineering, statistics, computer science, machine learning or a related field; A proven track record in the analysis, visualization, and interpretation of genomic data types including next-generation sequencing (NGS) data; Demonstrated ability to work closely with project teams and/or experimental collaborators to design studies and develop analyses to answer scientific questions; Familiarity with applying computational methods and bioinformatics tools to large- scale data including proficiency with Linux/Unix systems and high-performance computing environments; Experience with relevant analytical approaches and underlying statistical assumptions; Detail-oriented and self-motivated approach to problem solving with excellent analytical rigor
   
Text: Research Scientist I, Computational Genomics single-cell characterization (sc/snRNA-Seq); gene expression analysis (RNA-Seq, eQTL, Pathway/network analysis); bioinformatics engineering (programming, analytical pipeline development, data visualization, high-performance/cloud computing); statistical genetics (GWAS/PheWAS/Fine mapping/Gene burden testing); statistics and machine learning (traditional statistical analysis, decision theory, clustering, network analysis, text mining); evolutionary/population genetic sequence analysis (Population structure, Homology/Conservation inference) PhD or MS with experience in computational biology, statistical genetics, population genetics, bioinformatics, biomedical engineering, statistics, computer science, machine learning or a related field; A proven track record in the analysis, visualization, and interpretation of genomic data types including next-generation sequencing (NGS) data; Demonstrated ability to work closely with project teams and/or experimental collaborators to design studies and develop analyses to answer scientific questions; Familiarity with applying computational methods and bioinformatics tools to large- scale data including proficiency with Linux/Unix systems and high-performance computing environments; Experience with relevant analytical approaches and underlying statistical assumptions; Detail-oriented and self-motivated approach to problem solving with excellent analytical rigor
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