Bioinformatics Intern for Genomic Research in Oncology

2 weeks ago


South San Francisco, California, United States BioSpace, Inc. Full time
  1. Job Details
  2. About AbbVie

AbbVie is a global biopharmaceutical company with a mission to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow.

Job Description

This internship opportunity is a collaborative effort between the Computational Oncology, Precision Medicine, and Oncology Discovery Research groups at AbbVie's Genomics Research Center.

  • Consolidate pre-clinical and real-world data sets to create population cohorts for subsequent analyses.
  • Conduct bulk and single cell RNA-seq analysis to discover new targets and biological pathways.
  • Validate the identified hypotheses by cross-validating them in larger real-world data cohorts and conducting a thorough literature review.
  • Effectively communicate and present research progress to diverse working groups.

This internship will provide the selected candidate with the opportunity to conduct a meta-analysis of pre-clinical datasets to investigate the synergistic effects of antibody drug conjugates in combination with immunotherapeutics.

Qualifications

  1. Minimum Qualifications
  • Currently enrolled in university, pursuing a PhD in Computational Biology, Bioinformatics, Biostatistics, Immunology, Molecular Biology, Bioengineering, or a related field.
  • Must be enrolled in university for at least one semester following the internship.
  • Hands-on experience with NGS data analysis and proficiency in at least one general-purpose data science language.
  • Comfortable working with Linux and an on-premise high-performance computing environment.
Preferred Qualifications
  • Expected graduation date between December 2025 and July 2026.
  • Familiarity with cancer biology and immuno-oncology.
  • Proficiency in conducting bulk and single cell RNA-seq, differential gene expression analysis, and pathway enrichment analysis.
  • Experience working with version control environments, such as Git.
  • Interest in exploring innovative applications of AI/machine learning strategies for biological target discovery.
  • Demonstrated aptitude for critical thinking and problem-solving.
  • Self-motivated with the ability to execute tasks independently and with minimal supervision.


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