Postdoctoral Fellow - Biostatistics

A postdoctoral position is available in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center for bioinformatics and biostatistics methodology research. The main focus is research and publication. The primary focus will be developing novel methods for integrative analysis of 3D spatial transcriptomics data, deriving related computational and statistical methods, producing software for implementation, applying the methods to simulation and real data problems, and comparing the proposed tool with existing methodologies. The postdoctoral fellow will work under the supervision of Drs. Ziyi Li and Ken Chen on challenging and important 3D reconstruction problems that involve complex computational modeling, data analysis, and visualizations. All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations. LEARNING OBJECTIVES u2022 Gain expertise in statistical theory and application in 3D ST reconstruction, cancer genomics and immunotherapy studies, with a focus on omics integration and visualization. u2022 Develop advanced skills in integrative modeling approaches, including transformer, foundation models and other machine learning/AI methods, to analyze spatially resolved multi-omics data. u2022 Apply computational and statistical methods to real-world datasets from pancreatic and lung cancer patients to identify immune-related biomarkers and cell-cell interaction patterns. u2022 Strengthen programming proficiency in R and Python, with hands-on experience developing interactive tools using R Shiny for spatial data visualization and exploration. u2022 Engage in statistical methodology research in areas such as 3D spatial data integration, immune repertoire modeling, and precision oncology. ELIGIBILITY REQUIREMENTS Applicants must have a recent PhD in biostatistics/statistics/bioinformatics/computational biology/computer science from a reputed university/institute or be within 0-1 years of graduation. At least one first-author publication in a peer-reviewed journal stemming from PhD studies is required. A solid background in programming, statistical theory, bioinformatics applications, and the analysis of various high-throughput data modalities is required. Some experience with machine learning is desirable. Please send CV and information on three referees directly to . ADDITIONAL APPLICATION INFORMATION The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees and the public. 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. [

Created: 2026-01-26