Calico is seeking to advance on machine learning approaches for regulatory genomics, particularly developing and evaluating methods for analyzing regulatory sequence functions
Requirements
- Fluency in Python and practical experience building models with deep learning frameworks (PyTorch preferred)
- Strong computational fundamentals including machine learning, algorithms, data structures, and statistics
- Knowledge of molecular biology, genetics, or regulatory genomics
- Experience analyzing functional genomics data
- Experience working with DNA sequence models
Responsibilities
- Develop and evaluate machine learning models for regulatory genomics, focusing on model interpretability
- Collaborate with experimental scientists to validate computational predictions and derive biological insights from large-scale functional genomics data
- Communicate research to the broader scientific community through peer-reviewed publications, conference presentations, and open-source software
Other
- Currently pursuing a MS or PhD degree in Computer Science, Computational Biology, Bioinformatics, or a related quantitative field
- Self-motivated and detail-oriented with a collaborative mindset for working in cross-functional scientific teams
- Strong written and verbal communication skills, with an ability to present complex technical topics to scientific audiences
- Able to work in-person at our offices in South San Francisco
- Must be enrolled in a US university
