2027 Future Talent Program - Data, AI, and Genome Sciences - Intern
HybridNo visa sponsorship
Core
Develop statistical and computational methods/pipelines to utilize, analyze, interpret, and validate perturb-seq datasets and in silico perturbation predictions from single-cell foundation models.
Role type
PhD-level intern data scientist (AI/ML)
Builds
Statistical and computational pipelines for single-cell omics data analysis
Domain
Genomics + Machine Learning
Required skills
Python, single-cell omics data analysis, statistical methods, software design
Preferred skills
Biological foundation models, advanced statistics/Bayesian methods, PyTorch, scalable ML solutions, independent project leadership
Technologies
Python, PyTorch, perturb-seq, single-cell foundation models
Responsibilities
Develop approaches to assess and validate in silico perturbation predictions; implement scalable solutions for complex tasks
Seniority
Intern (PhD student)