2023 Experiential Internship- Computational Biology, Machine Learning(Massachusetts)

2023 Experiential Internship- Computational Biology, Machine Learning(Massachusetts)

Abbvie | Milwaukee (or Remote), WI, 53201, US
Salary Range:$32,000 – $37,000 Salary range estimated by Zippia

Posted a month ago

Apply Now

Description

ABOUT ABBVIE

AbbVie’s mission is to discover and deliver innovative medicines that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people’s lives across several key therapeutic areas: immunology, oncology, neuroscience, eye care, virology, women’s health, and gastroenterology, in addition to products and services across its Allergan Aesthetics portfolio.

For more information about AbbVie, please visit us at www.abbvie.com.

Follow @abbvie on Twitter, Facebook, Instagram, YouTube, and LinkedIn. 

COMPUTATIONAL BIOLOGY/MACHINE LEARNING INTERNSHIP OVERVIEW

Envision spending your summer working with energetic colleagues and inspirational leaders, all while gaining world-class experience in one of the most dynamic organizations in the pharmaceutical industry. This is a reality for AbbVie’s Experiential Interns.

The Computational Biology/Machine Learning Intern will work with the Immunology and Neuroscience Discovery CompBio teams of the Genomics Research Center (GRC) at AbbVie. The GRC is a multidisciplinary center of excellence with expertise in genomic technologies and their application, human genetics, genomic medicine, computational analysis, data integration, genome biology, and application of functional genomics.

In collaboration with two scientific mentors, the Computational Biology/Machine Learning intern will work to identify causal molecular determinants of Alzheimer's disease (AD). Human snRNA-seq data often has the limitation that while providing associative insights it does not improve the understanding of causal effects, as no large-scale time-series or treatment effect data is available from human cohorts. Integrating multi-omics single cell and AD GWAS data from human tissues can help resolve this limitation and provide causal associations. The project focuses on identification of cell type specific risk genes causally associated with AD but is expandable to other diseases. At the end of this project the intern will have gained knowledge in how to integrate genomics and multi-omics single cell data for target identification purposes.

KEY RESPONSIBILITIES INCLUDE:

  • Download and analyze published multi-omics single cell data from human AD brain

  • Implement RefMap model in R/Python and modify for application in single cell data; share code via AbbVie Github

  • Perform integration/analysis based on recent largest AD GWAS study, compare results with single cell eQTL data
  • Apply machine learning to causal modelling graph
  • MINIMUM QUALIFICATIONS

  • Currently enrolled in university, pursuing a Master’s or PhD in Computational Biology, Bioinformatics, or related field
  • Knowledge in R or Python programming
  • Experience in single cell transcriptomics data analysis
  • Strong written, verbal and oral communication skills

  • Strong self-organization skills

  • Excellent quantitative and analytical skills
  • PREFERRED QUALIFICATIONS

  • Knowledge in MATLAB programming
  • Experience in analyzing scATAC-seq data

  • Experience with GWAS/eQTL data analysis

  • Exposure to neuroscience projects
  • BENEFITS AND AMENITIES

  • Competitive pay

  • Travel, transportation and lodging support for eligible students

  • Opportunity to connect with AbbVie leaders and scientists
  • Exposure to cutting edge discovery concepts
  • EQUAL EMPLOYMENT OPPORTUNITY

    At AbbVie, we value bringing together individuals from diverse backgrounds to develop new and innovative solutions for patients. As an equal opportunity employer we do not discriminate on the basis of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information gender identity or expression, sexual orientation, marital status, protected veteran status, or any other legally protected characteristic.