Bioinformatician

Job ID:
Job date: 2018-05-04
End Date: 2018-07-03

Company : University of Massachusetts 

Country : United States 

Role : Technician 


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Job Description:
Under the direction of a member of the faculty, the Bioinformatician is responsible for the design, development, evaluation and iterative modification of a technical infrastructure to expedite the quantitative evaluation of data resulting from studies that are laboratory based. The position will entail the establishment and maintenance of applicable in-house bioinformatics resources and interaction with individual lab members on customized research projects, as well independent projects that are solely computational.

Responsibilities include:

  • Develop novel algorithms and software tools for integrated data analysis and visualization of high-throughput dataset including next-generation sequencing data and proteomics data when existing software packages are not available or adequate
  • Enhance/Maintain Bioconductor packages developed in house
  • Collaborate closely with PIs and their lab members on research projects including evaluating, selecting, and implementing when needed the best of breed computational solutions for the analysis of biological data
  • Develop rapid prototypes and custom scripts for one-off type of analysis requests
  • Properly document the procedures used in computational analysis and provide summary report of results suitable for inclusion in manuscript and grant applications

Responsibilities

MAJOR RESPONSIBILITIES:

  • Establish general bioinformatics resources for day-to-day use by members of the laboratory
  • In collaboration with a faculty member, generate customized programming solutions to improve user interaction with available bioinformatics resources
  • Assist in the implementation of programs for microarray analysis, high-throughput sequencing data analysis, cis regulatory motif identification, and multi-genome protein motif searches
  • Local establishment and customization of model organism genomic databases and tools for batch sequence analysis utilizing these resources
  • Interpret and present study results in support of laboratory members
  • Provide tabular and written summaries of approaches and analyses in a form suitable for inclusion in manuscripts or grant applications, as well as media for presentation at scientific meetings
  • Develop and implement custom bioinformatics programming solutions in collaboration with lab as necessary
  • Coordinate and collaborate with other research computing expertise at the Medical School as necessary
  • Participate in conference calls and data management meetings as needed
  • Perform other duties as required.

Qualifications

REQUIRED QUALIFICATIONS:

  • BS in computer science or a related discipline. Previous work experience desired, but not required; candidates with advanced degrees will be considered.
  • Strong background in statistical methodology, software languages and computer systems (Perl, C++, R, MySQL, etc.)
  • Experience in writing basic search algorithms and the ability to generate new algorithms and programs for custom data manipulation and analysis
  • Excellent communication skills, both oral and written, and interpersonal skills necessary to interact with a wide range of individuals


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Additional Info:
In the Department of Molecular Cell & Cancer Biology of University of Massachusetts Medical School, we have developed a core team of accomplished computational biologists who work side-by-side with bench scientists to evaluate, select and develop algorithms and software pipelines to analyze, annotate, visualize and integrate various biological datasets, including those generated from high-throughput experiments such as ChIP-seq, ChIP-chip, RNA-seq, PAS-seq, iCLIP-seq, ATAC-Seq, miRNA-seq, exon sequencing, GUIDE-seq, single-cell sequencing, shRNA and gRNA screening, and CRISPR genome editing. We are active contributors to the open-source open-development Bioconductor project. We have developed more than a dozen packages with various utilities, ranging from machine learning, peak calling, annotation, visualization, to gRNA design and evaluation.

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