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This position is open to US-citizens only.
Summary:
The University of Virginia Bioinformatics Core seeks a bioinformatics analyst. The analyst works with other core staff to manage and analyze large-scale datasets produced by next-generation sequencing. The analyst identifies opportunities and implements solutions for managing, visualizing, analyzing, and interpreting genomic data, including studies of gene expression (RNA-seq and microarrays), pathway analysis, protein-DNA binding (e.g. ChIP-seq), DNA methylation, and DNA variation, using Affymetrix, Illumina, Nimblegen, Agilent, Roche 454, Ion Torrent, and other high-throughput platforms in both human and model organisms. The analyst also implements solutions for microbial genetics and metagenomics workflows. The analyst works closely with the core director to assist in experimental design and provides expert consultation, technical, and scientific support for UVA investigators, and assists in outreach and training activities. The analyst organizes large-scale gene expression and sequencing data sets, manipulates and formats data with perl, python, or other scripting languages, uses established software to assess quality and analyzes data, schedules and runs jobs on a high-performance computing cluster, uses Unix and/or a scripting language to extract meaningful results from output, uses software or genome browsers for visualization, and implements custom data management and analysis solutions as necessary.
The successful candidate must have a demonstrated ability to translate biological questions into technical designs, and to identify, prioritize, and execute bioinformatics tasks to meet project goals and deadlines. This position has a shared effort of 50% with the UVA Center for Public Health Genomics.
Required: Strong knowledge of working in a Unix/Linux environment and experience with high-performance computing.
Familiarity with statistical computing, proficiency in scripting language, experience with relational databases.
Preferred: Experience with open-source software, tools, and databases for analyzing next-generation sequencing data (RNA-seq, ChIP-seq, DNA variation, DNA methylation).Experience working with next-generation sequencing data.
This position is open to US-citizens only.
Summary:
The University of Virginia Bioinformatics Core seeks a bioinformatics analyst. The analyst works with other core staff to manage and analyze large-scale datasets produced by next-generation sequencing. The analyst identifies opportunities and implements solutions for managing, visualizing, analyzing, and interpreting genomic data, including studies of gene expression (RNA-seq and microarrays), pathway analysis, protein-DNA binding (e.g. ChIP-seq), DNA methylation, and DNA variation, using Affymetrix, Illumina, Nimblegen, Agilent, Roche 454, Ion Torrent, and other high-throughput platforms in both human and model organisms. The analyst also implements solutions for microbial genetics and metagenomics workflows. The analyst works closely with the core director to assist in experimental design and provides expert consultation, technical, and scientific support for UVA investigators, and assists in outreach and training activities. The analyst organizes large-scale gene expression and sequencing data sets, manipulates and formats data with perl, python, or other scripting languages, uses established software to assess quality and analyzes data, schedules and runs jobs on a high-performance computing cluster, uses Unix and/or a scripting language to extract meaningful results from output, uses software or genome browsers for visualization, and implements custom data management and analysis solutions as necessary.
The successful candidate must have a demonstrated ability to translate biological questions into technical designs, and to identify, prioritize, and execute bioinformatics tasks to meet project goals and deadlines. This position has a shared effort of 50% with the UVA Center for Public Health Genomics.
Required: Strong knowledge of working in a Unix/Linux environment and experience with high-performance computing.
Familiarity with statistical computing, proficiency in scripting language, experience with relational databases.
Preferred: Experience with open-source software, tools, and databases for analyzing next-generation sequencing data (RNA-seq, ChIP-seq, DNA variation, DNA methylation).Experience working with next-generation sequencing data.