Join Our Lab

Members of the Zheng Lab work both independently and collaboratively, leading their own topics while sharing skills and benefiting from others’ expertise. The group fosters an energetic atmosphere that encourages enjoyment of science and motivates innovation in scientific discovery. We aim to promote an efficient, healthy scientific life where we are proud of our work, enjoy science and stay strong and healthy, both physically and mentally.

Available Positions

Graduate Research Assistants for Ph.D. Students (with/without seeking dissertation mentorship)

• Location: Houston local, other United States institutions, remote
• Fields: statistics, computer science, engineering, computational biology, quantitative science

Research Interns for Ph.D. Students

• Location: On-site (Houston local) or remote
• Fields: statistics, computer science, engineering, computational biology, quantitative science
• Requirement: Need to have at least one first, co-first, corresponding or co-corresponding publication or pre-print

Postdoctoral Fellows

• Candidates with backgrounds in statistics, computer science, engineering or bioinformatics research experience are preferred.
  
• We are especially looking for candidates with experience in developing new computational or statistical models, algorithms and software.
  
• Requirement: Need to have at least one first, co-first, corresponding or co-corresponding publication or pre-print
  

If you’re excited to contribute to cutting-edge research in computational cancer biology, we would love to hear from you!

How to Apply

Please send the following materials to Dr. Zheng via email:

• CV/resume
• Brief cover letter describing your relevant experience and motivations
• GitHub link to repository or materials demonstrating your programming skills
• Related research manuscripts/writing samples (if applicable, required for postdoctoral candidates)

Hiring projects (Updated Feb 2026)

1) AI Pathology Image Annotation and Modeling

Develop AI methods to annotate pathology images and connect tissue morphology with downstream molecular profiling and modeling. A follow-up effort focuses on developing deconvolution and harmonization models that enable accurate cross-patient and cross-organ comparisons of mixed-cell epigenomic profiles.

2) Agentic AI Workflows for Epigenomic Data Analysis

Build agentic AI workflows that automate and streamline epigenomic data processing, interpretation and reporting.

3) 3D Chromatin Organization and Gene Regulation

Study 3D genome organization and long-range regulation using integrative modeling across multiple genomic modalities.

4) Cross-Platform Cell Surface Protein Integration

Integrate protein measurements across single-cell and spatial platforms to improve cell-type/state characterization and downstream analysis.