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

• Location: Houston local, other U.S. institutions, remote
• Fields: Statistics, Computer Science, Engineering, Computational Biology, Quantitative Science

Research Interns for Ph.D. Students

• Duration: On-site or remote
• Fields: Statistics, Computer Science, Engineering, Computational Biology, Quantitative Science
• Must have a first or co-first or corresponding or co-corresponding published paper or a preprint online.

Postdoctoral Fellows

• Candidates with backgrounds in Statistics, Computer Science, or Engineering and Bioinformatics
• Biologists with strong computational skills are also encouraged to apply
• We are especially looking for candidates who have experience with new models, algorithms, and software construction
• Must have first or co-first or corresponding or co-corresponding published papers or preprints online.

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

How to Apply

Please send the following materials to yzheng8 AT mdanderson.org:

• 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 candidate)

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.