University of Southern California-posted 6 days ago
$54,080 - $77,687/Yr
Full-time • Entry Level
Los Angeles, CA
5,001-10,000 employees

There is an exciting new opportunity at the University of Southern California for highly motivated candidates committed to developing a career in genomics, epigenomics, or bioinformatics. The Postdoctoral Scholar – Research Associate will analyze in-house generated sequencing datasets and integrate multi-dimensional large-scale omics datasets available (dry lab). The trainee is expected to lead multiple research projects under the direction of the Principal Investigator and s/he will be a part of the collaborative research team at USC. Due to the collaborative, multi-lab nature of this project, it is essential that the applicant have excellent organizational and communication skills. The postdoctoral trainees will be encouraged to submit their own fellowship and grant applications during their period of training.

  • Installing and maintaining existing open-source software
  • Building computational infrastructure and systems to support the software
  • Writing novel scripts to analyze next-generation sequencing datasets
  • Developing web-based applications and tools to facilitate data analysis
  • Preparing presentations and publishing scientific manuscripts under the direction of the Principal Investigator
  • Responsible for teaching other lab members
  • Possess a PhD or be close to the completion of a PhD or DPhil in Computational Biology, Bioinformatics, Computer Science, or Data Science in a relevant field (e.g. Biology with strong quantitative training in bioinformatics)
  • Background and work knowledge in scientific computing, algorithms, and machine learning or statistics is required
  • Familiarity with R or Python, and the Unix (Linux) environment is required
  • Minimum education: Ph.D. or equivalent doctorate within previous three years
  • Minimum experience: 0-1 year
  • Minimum field of expertise: Directly related education in research specialization with advanced knowledge of equipment, procedures and analysis methods.
  • Skills in other programming languages (e.g. perl or java) is a plus
  • Experience in analyzing and annotating large omics data sets, both in exploratory and pipelined fashions is a plus
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