Computational Spatial Transcriptomics Jobs in Texas

Fully funded full-time postdoctoral fellow positions are available in Dr. Andrew H. Song's lab (opened in Jan.

2026) at the Department of Translational Molecular Pathology and the Institute for Data Science in Oncology, the University of Texas MD Anderson Cancer Center. We are seeking highly talented and motivated computational postdoctoral fellows with a strong background in computer science, statistics, mathematics, and bioinformatics with a passion for solving critical healthcare problems at truly large scale. Fellows will be mentored under close guidance from a PI with a strong track record of publishing in top-tier journals (Cell, Nature Medicine, Nature Cancer, Nature Reviews Bioengineering) and ML conferences (ICML, CVPR, NeurIPS, MICCAI).

This position offers an outstanding platform to grow your scientific independence, publish at the highest levels, and build a career making transformative impact in medicine. In addition, this is a great chance to help shape an emerging computational lab in one of the world's leading cancer centers. Dr.

Song's lab is dedicated to building next-generation AI tools for computational pathology, grounded in rigorous principles of statistical inference, with the overarching goal of deciphering multi-scale oncologic complexity and improving outcome prediction for cancer patients. The lab's research will focus on developing state-of-the-art foundation models and agentic AI frameworks capable of integrating diverse data modalities-including tissue images, spatial transcriptomics, spatial proteomics, and clinical reports-across multiple dimensions of clinical data (2D, 3D, and even 4D longitudinal datasets). By combining these innovations with advanced statistical approaches such as Bayesian inference, the lab aims to open new frontiers in computational pathology and precision oncology.

Based in the world's leading cancer center within the largest medical complex in the world (Texas Medical Center), the candidates will have direct access to one of the most comprehensive patient tissue and data repositories anywhere. In addition to the vibrant and rich cancer research ecosystem within TMC/Houston, the candidates will have exciting opportunities to collaborate extensively with external collaborators in academia (Harvard Medical School, Stanford, and numerous leading hospitals in Asia/Europe) as well as industrial partners to foster translational impact at scale. MD Anderson also provides a wealth of computational resources, including high-performance computing clusters tailored for biomedical research and on-demand access to the Texas Advanced Computing Center.

For more information, refer to Dr. Song's website at https://andrewhsong.com All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations. LEARNING OBJECTIVES Learn and master skills for in-depth profiling and distillation/fusion of heterogeneous multimodal high-dimensional data sources (tissue images and transcriptomics/proteomics/metabolomics data)

Gain extensive experience on developing and applying state-of-the-art AI frameworks in vision/language/omics. In addition to these research skills, the candidate will be trained heavily on efficient and clear communication with collaborators in clinical settings, mentoring junior trainees, publishing high-impact articles, and writing grants for career development. ELIGIBILITY REQUIREMENTS Candidates with a Ph.D

in Computer Science, Electrical Engineering, Statistics, Mathematics, Biomedical data sciences or a related field are encouraged to apply. 1. Strong computational skills - Proficient in python and pytorch with extensive experience of training/validating AI models (computer vision and LLM).

- Extensive experience in handling and analyzing tissue image data (H&E whole-slide images) and/or omics data (bulk-seq, spatial omics data) - Experience in large-scale, high-performance GPU cluster training and job handling - Experience with open-source codebases (Github, Hugging Face) and engagement with the developer community 2. Strong publication background - Proven track record of journal publications (or submissions) and/or premier ML conferences 3. Strong communication, writing, and collaboration ability.

Ability to conduct well-organized and reproducible research workflow is a must. ADDITIONAL APPLICATION INFORMATION In addition to submitting the application, please email the following to asong2@mdanderson.org (1) Cover letter on the candidate's research interest, career goals, and how this can align with Dr. Song's new research lab direction

(2) CV or Resume, with reference to Github/Hugging Face repository (if available). (3) 2~3 representative publications, with concise description of the candidate's contribution to each piece (4) Email address for three references. POSITION INFORMATION 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

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