Postdoctoral Fellow - Biostatistics

The Department of Biostatistics at The University of Texas MD Anderson Cancer Center is seeking a highly motivated Postdoctoral Fellow to conduct methodological research in spatial transcriptomics and computational biology.

The primary focus of this position is to develop novel computational and statistical methods for the analysis and integration of spatial omics data. Potential research directions include 3D reconstruction and modeling of spatial transcriptomics data, inference of cell-cell interactions and spatial cellular organization, integration of spatial and single-cell omics data, and AI/ML approaches for spatial omics analysis. The postdoctoral fellow will have the flexibility to develop new research directions based on their background and interests.

The fellow will develop novel methodologies, implement scalable computational tools and software, evaluate the proposed methods through simulations and benchmarking against existing approaches, and apply them to large-scale spatial and single-cell datasets in collaboration with cancer researchers.

The postdoctoral fellow will work under the supervision of Dr. Ziyi Li in the Department of Biostatistics at MD Anderson Cancer Center. The position provides opportunities to work on challenging methodological problems involving spatial omics, computational modeling, machine learning, statistical analysis, and scientific visualization, with a strong emphasis on methodological innovation, software development, and high-impact publications.

All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.

• Gain expertise in computational and statistical methodology for single-cell and spatial omics, with a particular focus on spatial transcriptomics and the characterization of tissue architecture and cellular organization.
• Develop novel computational and statistical methods for spatial omics, including 3D reconstruction and modeling of spatial transcriptomics data, inference of cell-cell interactions and communication, and integration of spatial and single-cell datasets.
• Develop advanced skills in computational modeling, machine learning, and AI approaches for analyzing large-scale, high-dimensional spatial omics data.
• Apply and evaluate newly developed methods using simulation studies, benchmarking against existing approaches, and applications to real-world spatial and single-cell datasets from cancer research.
• Strengthen programming and scientific computing skills in R and Python, with hands‑on experience in developing scalable, reproducible, and user-friendly software tools for spatial omics analysis and visualization.
• Gain experience in translating methodological innovations into open-source software and high-impact scientific publications, while collaborating with biostatisticians, computational scientists, and cancer researchers on challenging biomedical problems.

Applicants should have a recent PhD in Biostatistics, Statistics, Bioinformatics, Computer Science, Computational Biology, Biomedical Informatics, or a closely related quantitative field. Candidates with strong methodological and computational training are particularly encouraged to apply.

Applicants should have a strong background in at least one of the following areas: statistical methodology, bioinformatics, machine learning/AI, computational biology, or high-dimensional data analysis. Experience with single-cell or spatial omics data is highly desirable but not required. Candidates with strong backgrounds in machine learning, statistical modeling, or algorithm development who are interested in transitioning into spatial omics research are also encouraged to apply.

Strong programming skills in R and/or Python are required. Experience with scientific software development, high-performance computing, deep learning frameworks, or analysis of large-scale genomic datasets is a plus. Applicants should demonstrate strong research potential through peer-reviewed publications, including at least one first-author publication or equivalent evidence of substantial independent research contributions.

The ideal candidate will be highly motivated to develop novel computational and statistical methodology, work collaboratively with biostatisticians and biomedical researchers, and publish high-quality methodological and applied research.

MD Anderson offers full-time postdoc positions with a…