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A full-time postdoctoral fellow position is available in Professor Wenyi Wang's lab at the Department of Bioinformatics and Computational Biology, the University of Texas MD Anderson Cancer Center. We are seeking a highly self-motivated postdoctoral candidate with experience in cancer research and strong analytical skills in single-cell RNA-seq data. Statistical modeling expertise in machine learning and/or Bayesian models is preferred.

This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data, from cancer patient cohorts and interpretation. The candidate will work closely with our experienced clinician collaborators at MD Anderson, contributing to revealing the underlying mechanism behind the response heterogeneity of cancer and developing novel therapeutic targets. LEARNING OBJECTIVES Analyze next-generation sequencing (NGS) data, including: Bulk DNA-seq and RNA-seq Single-cell RNA-seq Spatial transcriptomics Develop computational and statistical methods for multi-omic and single-cell data Integrate diverse molecular data to study tumor evolution and therapeutic response Collaborate with MD Anderson clinicians and basic scientists Investigate mechanisms of tumor heterogeneity and resistance Mentor graduate and undergraduate students and rotation trainees Present research at scientific conferences and contribute to peer-reviewed publications ELIGIBILITY REQUIREMENTS Required: Ph.D

in bioinformatics, computational biology, statistics, computer science, or related field Proficiency in R or Python Minimum one year of experience in computational biology or cancer genomics Experience with high-performance or cloud computing (e.g., HPC, AWS, GCP) At least one first-author peer-reviewed publication Strong communication and scientific writing skills Preferred: Hands-on experience with single-cell and spatial transcriptomic analysis Familiarity with multi-omic data integration workflows Cancer biology background or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented and committed to reproducible research Strong team player with mentoring and collaborative experience Critical thinker with strong problem-solving abilities ADDITIONAL APPLICATION INFORMATION Dr. Wang's laboratory conducts cutting-edge research to understand the evolution of cancer transcriptomes through DNA-RNA dynamics, aiming to uncover mechanisms of cancer initiation, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes

See further information at the lab webpage: https://odin.mdacc.tmc.edu/~wwang7. 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 Apply