• Bioinformatics Research Scientist (Statistical Genetics/Genetic Epidemiology)

    Job ID
    F1123
    Department
    Center for Applied Bioinformatics
    Category
    Bioinformatics
    Job Location
    US-TN-Memphis
  • Overview

    The Center for Applied Bioinformatics (CAB) is anticipating an exciting career opportunity for a highly motivated and creative individual to join our Genetics Group. As a Bioinformatics Scientist (Statistical Genetics/Genetic Epidemiology), your responsibilities will include genotype-phenotype association, ancestry inference, QC and development/testing of analytic pipelines for familial and population genetics projects. We are seeking candidates with expertise in identifying causal or susceptibility factors (including genetic, epigenetic and environmental factors) that increase disease risk or modify disease progression, treatment responsiveness, and survivorship. We have ongoing sequencing projects in sickle cell disease, amyotrophic lateral sclerosis, hodgkin’s lymphoma, acute lymphoblastic leukemia, and other diseases. There are opportunities to work on ongoing GWAS and sequencing projects as well develop new projects in areas such as statistical methods for genetic studies. We are closely integrated with the Transcriptomics and Epigenetics Groups in the CAB, allowing for cross-training opportunities and collaboration.

     

    Our group works closely with investigators in St. Jude Children’s Research Hospital Comprehensive Cancer Center, the first and only NCI designated cancer center devoted to children (https://www.stjude.org/research/comprehensive-cancer-center.html), in order to discover and understand the genetics and biology of pediatric cancers. Our projects extend to nonmalignant hematological disorders such as the Sickle Genome Project (http://sickle-cell.stjude.cloud/) and related blood  disorders (https://www.stjude.org/treatment/disease/other-blood-disorders.html)  and neurological diseases. We have close collaboration with the Department of Biostatistics (https://www.stjude.org/research/departments-divisions/biostatistics.html) for expanding methodological approaches.

     

    Applicants should have training in statistical genetics, genetic epidemiology, or bioinformatics with in-depth understanding of computational genomics and disease modeling tools, including association and rare variant methods. Experience with data assimilation across WGS, RNAseq, and ChIPSeq datasets is desirable. Should be familial with public genomics datasets such as 1000G, gnomAD, TopMed, dbGAP, GTXe Portal.

    Substantial skills in computer programming, including R or Python, are required. Java web development skills is desirable.

    Responsibilities

    • Work with wet lab scientists or clinicians to provide statistical guidance on study design and analysis approaches

    • Assist with preparation of project reports, presentations, and manuscript of analysis results, including detailed description of statistical genetic methods

    • Participate in the development of operations and procedures for the collection, editing, verification and management of data.

    • May assign and oversee the work of junior analysts

    Minimum Education

    PhD in Molecular Biology, Biochemistry, Computer Science, Statistics, Mathematics, Bioinformatics or related field required.

    Minimum Experience

    PhD in Molecular Biology, Biochemistry, Computer Science, Statistics, Mathematics, Bioinformatics or related field required. Prior experience must include research related to bioinformatics (such as analysis of DNA and RNA sequence data, microarray, SNPs, imaging data, proteomics data, or biological pathways; development of algorithms, statistical methods, or scientific software).

     

    --OR--

     

    If PhD training did not include bioinformatics research, will require a minimum of two (2) years of pre- or postdoctoral research experience in Computational Biology or Bioinformatics.

     

    Experience with programming languages such as Perl, C, or Java required. Experience with Python and knowledge on statistical analysis package such as R, SAS and Matlab is highly desired.

     

    EEO Statement

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