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.

LEARNING OBJECTIVES

• 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.

ELIGIBILITY REQUIREMENTS

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.

ADDITIONAL APPLICATION INFORMATION

Please send a CV and contact information for three references directly to zli16@mdanderson.org.

POSITION INFORMATION

MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition

Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.

This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

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