2026 Summer Intern - CS-CoE (Computational Biology & Single-Cell/Spatial Proteomics)
About The Position
Our group develops computational methods that turn high-dimensional data into actionable biological insight. We work at the intersection of machine learning, statistical modeling, and experimental biology, with a focus on robust and reproducible analytics for modern single-cell modalities. This internship will contribute to methods development for network inference using emerging single-cell and spatial proteomics data, with the goal of producing a publication-quality methods study and an internally reusable analysis pipeline. This internship position is located in South San Francisco, California On Site.
Requirements
- Must have attained a Master's Degree.
- Must be pursuing a PhD (enrolled student).
- Computational Biology, Bioinformatics, Data Science, Statistics, Computer Science, Biomedical Engineering, Systems Biology (or related quantitative field).
- Strong Python skills for data analysis (NumPy/Pandas/scverse-ecosystem).
- Foundations in statistics and/or machine learning (regression, probabilistic modeling, evaluation).
- Experience working with high-dimensional biological data (single-cell preferred).
- Ability to write clean, reproducible code (version control, documentation, notebooks/pipelines).
Nice To Haves
- Familiarity with representation learning / deep learning (e.g., VAEs; PyTorch/JAX).
- Experience with proteomics and/or spatial omics data analysis.
- Experience with benchmarking and metrics design; comfort with HPC/cluster environments.
- Excellent communication, collaboration, and interpersonal skills.
- Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
Responsibilities
- Develop FAVA-X, a cross-modality extension of the FAVA network-inference framework, enabling functional network inference from single-cell and/or spatial proteomics while accounting for modality-specific noise characteristics and missingness.
- Evaluate VAE-based (and alternative) representation learning approaches to stabilize inferred networks, improve robustness across subsamples, and enable uncertainty quantification of network edges.
- Design and implement a multimodal network integration strategy (e.g., RNA + protein; single-cell + spatial) and systematically assess whether integration improves edge stability, biological coherence, and interpretability.
- Build a reproducible benchmarking pipeline using public datasets and standardized evaluation metrics, including reproducibility across data splits, pathway and functional coherence, cell-type specificity, and predictive or held-out validation.
- Communicate results through a publication-ready methods report, figures suitable for a manuscript or preprint, and a well-documented, reusable codebase (notebooks and/or packaged pipeline).
Benefits
- Intensive 12-weeks, full-time (40 hours per week) paid internship.
- Program start dates are in May/June (Summer)
- A stipend, based on location, will be provided to help alleviate costs associated with the internship.
- Ownership of challenging and impactful business-critical projects.
- Work with some of the most talented people in the biotechnology industry.
- paid holiday time off benefits
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Number of Employees
5,001-10,000 employees
