Postdoctoral Fellow – Imaging Technology and Compound Screening, Regev & Singh Labs
$100,000–$106,500 year
On-siteSouth San Francisco, California, United States
Job Summary
Develop and optimize high-plex imaging assays to capture multiplexed readouts of cell state and morphology. Design and execute large-scale compound screens across diverse, disease-relevant model systems, generating systematic, multimodal readouts of cellular response. Apply single-cell and multi-omics style analyses to build maps of perturbation relationships and prioritize promising compounds. Develop computational approaches that expand information extracted from each sample, leveraging laboratory automation and high-throughput workflows to iteratively design, run, and learn from screens at scale. Collaborate with experimental and computational scientists across departments to present work to project teams and contribute to high-impact scientific publications.
Required Qualifications
- Ph.D.
- Proven track record of excellence in single-cell genomics, imaging, bioengineering, biophysics, quantitative biology, or a related field
- Hands-on experimental expertise
- Advanced at least one key research project as evidenced by a first-author paper published or accepted in a leading peer-reviewed journal
- Strong hands-on experience in microscopy and quantitative imaging
- Experience with multiplexed, high-content, or optical pooled screening approaches
- Computational expertise spanning image analysis and machine learning
- Track record of developing or adapting methods
- Programming fluently in Python or a comparable language
- Independent scientist
- Outstanding communication skills
- Strong passion and commitment to science
- Works well within a team
- Excited about building new methods to screen compounds at scale
- Excited about discovering what drives cellular function and activity
- Must be available for weekend shifts
- Must be able to lift 50 lbs
Desired Qualifications
- Experience developing or deploying multiplexed imaging assays
- Experience with AI/ML applied to imaging or other high-dimensional biological data
- Experience with laboratory automation, liquid handling, and high-throughput experimental workflows
- Experience analyzing large-scale perturbation datasets and integrating multimodal data
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