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Cambridge, MA

Internship

Hybrid

Intern

Merck offers a 10–12-week internship in its Research & Development Division’s Data, AI, and Genome Sciences Department. The intern will develop statistical and computational methods for analyzing perturb-seq datasets and evaluating in silico perturbation predictions from single-cell foundation models.

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Responsibilities

Develop statistical and computational methods/pipelines to utilize, analyze, interpret, and validate perturb-seq datasets
Develop approaches to assess, interpret, and validate in silico perturbation predictions from single-cell foundation models

Qualification

Required

Must be currently enrolled in a PhD degree in Computer Science, Statistics, Bioinformatics, Computational Biology, or related technical field
Demonstrated experience with single-cell omics data (e.g., scRNA-seq, perturb-seq)
Excellent software design and development skills and strong proficiency in Python
Excellent communication skills and ability to work collaboratively in a multi-disciplinary team

Preferred

Motivated to impact biological projects with independent project leadership experience
Experience with biological foundation models (e.g., single-cell foundation models)
Experience with advanced statistics and/or Bayesian methods
Proficiency in modern ML frameworks (e.g., PyTorch) and experience implementing scalable solutions for complex tasks
Publications in journals or conferences