BACKGROUND

RESPONSIBILITIES

• Implement, design, develop, and innovate current and emerging computational biology and bioinformatics algorithms to analyze, manage, interpret, visualize, and illustrate original scientific data
• Enter into scientific collaborations with physicians and scientists that include the potential for authorships and acknowledgements in publications
• Gather detailed requirements from stakeholders and identify existing tools to perform the novel analyses or develop algorithms/tools to perform the analysis
• Document and manage collaborative and consultant assistance and training provided to researchers
• Provide on-site, on-demand support and troubleshooting to researchers in the use of computational biology software
• Research, design, and deliver training materials, courses, presentations, and workshops to effectively communicate, promote, and advance computational biology techniques and software usage by NIH researchers
• Remain abreast on current and emerging computational biology technologies and tools
• Partner with software developers to integrate genomic software solutions within enterprise platforms

REQUIREMENTS

• Ph.D. (or other graduate degree with equivalent experience) in computational biology, bioinformatics, genetics or related life, physical, or computational sciences with at least one publication demonstrating the use or development of computational biology applications
• Good understanding of genomics, molecular biology, and technologies such as PCR and next-generation sequencing
• Demonstrated proficiency in the analysis of large-scale genomic sequencing data (especially variant analysis from whole genome or whole exome sequencing data, but also RNA-seq, genome assembly) and experience with a broad spectrum of relevant open-source software or pipelines (BWA, TopHat, STAR, GATK, SnpEff, VEP, DESeq, etc.)
• Experience working with relevant genomic databases and browsers and their annotations (NCBI, Refseq, Ensembl, ClinVar, HGMD, IGV, UCSC Genome Browser)
• Proficiency in the use of UNIX/Linux and its command-line environment, including scripting (shell, Perl, Python, Ruby, Tcl/Tk, etc.)
• Experience with a high-performance parallel computing environment (e.g., SGE, PBS)
• Familiarity with common methods of statistical analysis using R, SAS, or related software
• Strong interpersonal, presentation, written, and oral communication skills to convey computational biology principles and concepts to non-specialists in a clear and precise manner and advise on relevant software and tools with a dedication to customer satisfaction
• Ability to work independently or as part of a multi-disciplinary team
• Excellent troubleshooting and problem-solving skills, including the ability to learn new software quickly
• Ability to concurrently work on multiple complex projects with effective time management skills, a high level of personal and professional drive and initiative, and attention to detail

PREFERENCES

• Experience with genomic diagnosis of rare diseases and filtering variants using family-based genetic models (e.g., autosomal recessive/dominant, compound heterozygous, etc.), including experience with relevant related tools for finding disease-causing variants (e.g. VAAST/pVAAST, GEMINI)
• Experience with structural variation analysis from next-generation sequencing data
• Experience constructing pipelines in open architecture platforms (especially GATK-Queue, but also Snakemake, Bpipe, others), including end-to-end tasks for NGS analysis tools
• Familiarity with algorithms and algorithm development for bioinformatics, including composing, editing, and compiling open-source code
• Familiarity with problems and bottlenecks associated with storage and management of genomic-scale data
• Familiarity with the use of open-source bioinformatics applications employing ontologies, pathways, and/or networks, at both the individual gene and genomic scales
• Strong background in molecular/cellular biology, immunology, and/or virology, including "bench" experience
• Ability to propose and develop new methodologies for analyzing biomedical data

HOW TO APPLY

POLICY