Post Doc - Open Rank
$62,232–$75,564 year
On-siteWorcester, Massachusetts, United States
Worcester, Massachusetts, United StatesOn-siteFull Time$62,232–$75,564 yearLarge
Full TimeLarge
Job Summary
Develop accurate, scalable algorithms to infer multi-modal, condition-dependent causal networks from high-dimensional single-cell datasets containing millions of samples. Apply these computational models to existing and new data to uncover biological principles governing cellular behavior and state transitions. Build open-source software tools for the community and disseminate findings through peer-reviewed publications and academic presentations. Collaborate with interdisciplinary team members to test rapid ideas and reverse-engineer complex gene regulatory systems.
Required Qualifications
- strong quantitative skills
- curiosity about complex systems
- design, implement, and apply new computational and statistical models
- reverse-engineer causal networks from noisy, high-dimensional, multi-modal data
- Develop accurate and scalable algorithms for inferring multi-modal, condition-dependent networks from datasets with millions of samples (cells) between tens of thousands of nodes (genes and genetic features)
- Apply these algorithms on existing and new datasets to uncover biological principles and insights across molecular, cellular, and population levels
- Build open-source, user-friendly software tools for the community
- Disseminate findings through peer-reviewed publications, user-friendly software packages, and academic presentations
- Collaborate with other group members and research groups as needed
Desired Qualifications
- experience with Perturb-seq (interventional single-cell CRISPR screens)
- experience with joint scRNA-seq + scATAC-seq
- experience with population-scale scRNA-seq
- experience with machine learning
- experience with causal inference
- experience with statistics
- experience with algorithms
- No prior biomedical training is required
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