Immunology Bioinformatics Intern PhD

Company Description

Abb Vie's mission is to discover and deliver innovative medicines and solutions 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, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about Abb Vie, please visit us at  Follow @abbvie on X, Facebook, Instagram, You Tube, Linked In and Tik Tok.

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 Abb Vie Interns.

Abb Vie’s QM&G is a center of excellence for bioinformatics, genomic sciences, human genetics and pharmacogenomics. The QM&G works across all R&D including discovery and clinical development, and multiple areas such as process sciences, search and evaluation and corporate strategy. The QM&G plays an integral role towards our goal of developing world class genetics and genomics research, focusing on finding the right targets and helping us better understand not only human disease biology but also the behavior of and response to our drugs in clinical trials.

The Bioinformatics for Immunology, Genetics, and Genomic Sciences (BIGGS) within QM&G is responsible for providing computational solutions and conducting computational research in collaboration with Discovery and Precision Medicine teams to facilitate target and biomarker discovery and development across Abb Vie’s Immunology portfolio. We partner with clinical scientists across discovery and development to identify gene signatures and pathways operant in human diseases through interrogation of complex multi-omics data sets generated from clinical samples.

We are seeking a highly motivated intern to lead a project aimed at integrating and analyzing genotype and phenotype data from different database sources to uncover genetic variants associated with adverse events, using robust real-world data (RWD) sources. The candidate will collaborate with computational biologists across the network and be integrated into research project teams focused on immunology, oncology, epidemiology, and statistics, analyzing multimodal RWD datasets to gain insights into immunological diseases.

This role is based in Cambridge, Massachusetts.

• Conduct extensive literature searches to identify and understand existing knowledge gaps related to adverse events.
• Conduct genome-wide association analyses to identify genetic variants associated with target traits.
• Validate significant findings through independent cohorts and functional annotation.
• Collaborate with multidisciplinary teams to integrate findings and contribute to identify specific patient groups with unmet needs.
• Document research processes and findings, ensuring clarity and accessibility for future projects and analysis.

Minimum Qualifications

• Currently enrolled in university, pursuing a PhD in bioinformatics, genetics, genetic epidemiology, computational biology or other related field.
• Must be enrolled in university for at least one semester following the internship.
• Expected graduation date between December 2026 and July 2027.
• Strong informatics skills for analyzing and visualizing genomic data, such as Whole Exome/Genome Sequencing; as well as surveillance health records.
• Knowledge of immunology, molecular biology, and population genetics.
• Proven experience in conducting GWAS and handling UK Biobank or OMOP data structures and/or other large genomic databases.
• Proficiency using R is required, and familiarity with cloud infrastructures and databases.

Preferred Qualifications

• Exposure to courses like causal inference, time-related bias, survival analysis.
• Experience in handling adverse events is a plus.
• Experience in immunology and/or oncology is preferred.
• Strong problem-solving and collaboration skills.
• Ability to communicate effectively with biologists, statisticians and other computational scientists.