Graduate Research Assistants

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

Research Interns

• Duration: Onsite or remote up to 6 months
• Fields: Statistics, Computer Science, Engineering, Computational Biology, Quantitative Science

Postdoctoral Fellows

• Candidates with backgrounds in Statistics, Computer Science, or Engineering and genomic research experience
• Biologists with strong computational skills are also encouraged to apply

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)

1. [Epigenomics + Statistics/ML + Cancer Biology]

RNA PolII profiling on the formalin-fixed paraffin-embedded (FFPE) samples provides a cost-effective and robust approach to generating critical data for cancer research and motivating new association and prediction models with patient phenotypes. We have several projects associated with this new technology:

• Normalization model designed for tumor tissues
• Aneuploidy calling for FFPE-CUTAC data
• Epigenomic marker and the cancer phenotype association modeling
• Computational processing and analysis pipeline

2. [Epigenomics + Statistics + Immunology]

Single-cell epigenomics data are known for their ultra-sparsity. Denoising and imputation models are needed to gain useful cell information and integrate it across epigenomic markers.

3. [3D genomics + Statistics]

Investigating the three-dimensional chromatin organization and the long-range gene regulation through multimodality integrative modeling and accompanying software development, leveraging data across transcriptomics, epigenomics and 3D genomics.

4. [Proteomics + ML]

Cell surface protein measurement can provide deeper and standardized single-cell cell-type annotations and status descriptions. The project integrates CITE-seq, Flow Cytomery and Spatial Proteomics data across the study and platform for joint disease analysis. Machine learning and image processing skills are needed.

5. [Immunotherapy + Computational Analysis]

Statistical modeling and computational analysis of immunological and immunotherapeutic Studies using multi-omics bulk and single-cell genomics data to decipher key genotypic and phenotypic features that drive efficacy versus toxicity in CAR-T cell immunotherapy.