The He Laboratory, newly established in the Department of Pathology at the University of California, San Francisco (UCSF), is seeking a highly motivated postdoctoral researcher to lead one or more of the three research directions:
- Develop new statistical and deep-learning models and algorithms to better analyze spatial transcriptomic, scRNA-seq, scATAC-seq, or/and scMultiomic data. Current projects include automated cell class annotations, large-scale atlas integration, multimodal data integration, signal enhancement and data imputation. None of these are solves problems, yet!
- Data science projects: scRNA and spatial omic analyses in various areas such as embryo development, hematopoiesis, lung biology, limb development, organoid biology and cancer biology; gene regulatory network construction based on multiomic data.
- Streamlining routine analyses by building computational pipelines; wrapping up in-house scripts into packages to serve broader communities.
These three directions are designed to interact synergistically, with the primary goal of building a strong CV with impactful publications to support the candidate's development toward an independent career. The ideal candidate should be able to drive their own projects and lead efforts with lab members to develop project leadership skills, collaborative expertise, and strong work ethics.
Research Environment: Our previous work has leveraged in-house computational approaches to build cell atlases of developing human tissues with unprecedented resolutions (Mouse limb atlas, human limb atlas, human lung atlas, human lung immune atlas). As the scales of data increases and the spatial and multiomic technologies advance, more sensitive discoveries can be made with more optimized computational methods. Our lab is diligently working on developing creative models and algorithms to solve key problems in tissue heterogeneity and cell fate decision, as well as on synthesizing first-hand ground-truth annotations to guide the algorithm development.
The He lab is part of the Department of Pathology and a member of ImmunoX, Cancer Center, Institute of Human Genetics, and the Institute of Regeneration Medicine. Located at the Parnassus campus, we enjoy scenic views of San Francisco, including the Golden Gate Bridge and the Sutro Forest. We are a highly responsive team with fast-paced interactions and flexibility to support young investigators in maximizing their creativity and efficiency at work.
Required Qualifications:
- Ph.D. in Computational Biology, Computer Science, Statistics, or related fields
- Strong programming skills in Python, R, or similar languages
- Solid background in linear algebra and statistics
- Track record of scientific publications
Preferred Qualifications:
- Ability to critically assess existing computational biology packages with biological insights
- Experience with machine learning and statistical modeling
- Background in genomics or molecular biology
- Experience with software development and package creation
To Apply: Please send the following materials to Peng’s email address:
- Cover letter describing your research interests and career goals
- Detailed CV including:
- Publications and technical skills
- GitHub profile link
- Undergraduate GPA and course work record (or transcript) as attachment
- Contact information for three references
UCSF offers competitive salary and comprehensive benefits. Salary will be commensurate with experience and based on UCSF postdoctoral scholar salary scales.
UCSF is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or protected veteran status.
Email: [email protected]