Postbaccalaureate Program in Quantitative Biology (Postbacc-QB)

What is a Postbacc?

A non-degree, 1–2 year, mentored research experience after a bachelor’s or master’s degree. Postbaccalaureates gain structured training, coursework, and hands-on research to prepare for PhD programs and research careers.

Why Postbacc-QB?

Immersive research in quantitative oncology labs
Coursework and mentoring toward PhD readiness
Community across computational biology and biostatistics
Proximity to clinical impact at a leading cancer center

Mission

Prepare recent graduates for competitive PhD/MD-PhD/MD programs in oncology.
Provide a non-clinical, full-time, paid research experience with benefits.
Pair each trainee with a faculty mentor to create an individual development plan aligned to goals.
Fill positions via partner-institution nominations on a fall cycle.
End with a capstone—research presentation and/or manuscript demonstrating readiness.

Trainee eligibility

BA/BS or MS in a relevant field earned within the past 2 years.
Strong record in quantitative/biomedical areas (e.g., bioinformatics, biostatistics, genetics, cell biology, epidemiology, public health, radiation oncology).
No doctoral degree (PhD, MD, DVM, MBBS, or equivalent).
Not currently enrolled in a degree program, residency, or other clinical training.
If already admitted to grad/medical school, provide written deferral permission (up to 1 year).

Quantitative Biology at MD Anderson

The study of biostatistics, bioinformatics, systems biology, and genomics centers on developing and applying statistical and mathematical models in close collaboration with biomedical researchers. Collaborating with UT GSBS Program in Quantitative Sciences, our goal is to train researchers who design rigorous studies and formulate quantitative models of biological systems, advancing our understanding of cancer biology and disease processes.

Research highlights include:

🔬 Single-cell & Spatial: Single-cell RNA/ATAC sequencing, spatial transcriptomics & proteomics, tumor microenvironment mapping.

🧬 Multi-omic Integration: Epigenomics and chromatin architecture, 3D genome organization, proteogenomics.

💻 Computational Modeling: Artificial Intelligence & deep learning, biological networks & pathways, gene–environment interactions, predictive oncology & risk modeling.