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  • Postdoc in Bioinformatics/Biostatistics at UNC Charlotte, NC, USA

    The Luo group at UNC Charlotte Dept. of Bioinformatics is seeking a new member as a postdoc researcher. The new members will develop statistical/computational methods/tools for high throughput omics (genome, transcriptome, proteome, metabolome etc) data integration, analysis and visualization; apply them to complex diseases/traits and biomedical problems. He/she will participate in the Pathview project. Pathview is an open-source package that maps, integrates and renders a large variety of biological data onto pathways, and produces interpretable graphs with publication quality. Pathview was quickly adopted and widely used by thousands of scientists worldwide. With a major NSF grant recently awarded, Pathview will be fully developed as a systematic solution for pathway based analytics and visualization.
    For more information about the Pathview and related projects:

    Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

    GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.


    Technical skills:
    Solid statistics training
    Genetics/genomics data analysis (esp. GWAS, Whole Genome/Exome Studies), NGS data analysis, sequence analysis
    R/Bioconductor
    Unix/Linux shell
    Python or Perl
    Version control: svn and git
    R package (or software) development is a plus

    Other qualifications:
    Excellent communication and problem-solving skills, attention to detail
    Ability to work independently and in a team
    Self-motivated and disciplined, time and project management skills
    Enjoy computational/statistical method development, data analysis
    Proven research/development experience, publication records

    Education:
    PhD (or Master + 3 years working experience) in statistics/biostatistics, bioinformatics, computational genetics, computer science, or related fields.

    Start Date:
    As soon as possible

    Application:
    Please send a CV, a cover letter and the names of 2-3 references to: [email protected]
    You may also include 1-2 representative papers/manuscripts.

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