Machine Learning Computational Biology Jobs in Texas

The University of Texas MD Anderson Cancer Center seeks an outstanding Postdoctoral Fellow to join the Department of Gastrointestinal Medical Oncology in advancing foundational artificial intelligence (AI) models for oncology. This position is embedded within MD Anderson's Moon Shots Program, an institutional initiative aimed at accelerating scientific discovery and translational impact to significantly reduce cancer mortality. The successful candidate will contribute to the development of next-generation multimodal AI systems that integrate diverse clinical and biological datasets to improve patient outcomes, enhance clinical operation, and advance precision oncology.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
-Develop, refine, and validate foundational AI models using large-scale multimodal oncology datasets.
-Integrate heterogeneous data sources, including electronic health records, digital pathology images, radiology data, bulk and single-cell omics, and real-world clinical outcomes.
-Design and implement novel computational frameworks for therapy response modeling, treatment optimization, clinical trial matching, and patient care enhancement.
-Collaborate closely with clinicians, computational scientists, biologists, and disease groups across MD Anderson.
-Disseminate research findings through peer-reviewed publications and presentations at national and international scientific meetings.
-Assist in grant development and project coordination as needed.
ELIGIBILITY REQUIREMENTS
- PhD in Computer Science, Computational Biology, Bioinformatics, Electrical Engineering, Biomedical Engineering, or a related quantitative discipline.
- Demonstrated expertise in machine learning or deep learning, including familiarity with large language models, multimodal architectures, or generative AI.
- Proficiency in Python and modern machine learning frameworks (e.g., PyTorch, TensorFlow, JAX).
- Experience working with biological, clinical, or other high-dimensional datasets.
Preferred:
-Background in oncology, cancer biology, immunology, or translational research.
-Experience with foundational model development, self-supervised learning approaches, or large-scale distributed training.
-Familiarity with EHR data structures, digital pathology workflows, or multi-omics integration.
-Strong publication record demonstrating rigor, innovation, and independence.
ADDITIONAL APPLICATION INFORMATION
Access to one of the richest and most comprehensive cancer datasets worldwide, enabled by MD Anderson's status as the top-ranked cancer center with the nation's largest oncology patient volume.
• Integration into the Moon Shots Program, providing unique opportunities for high-impact translational research, cross-disciplinary collaboration, and accelerated clinical application.
• A highly collaborative and well-resourced environment with strong institutional support for AI, data science, and precision oncology initiatives.
• Competitive compensation and benefits in accordance with NIH and MD Anderson guidelines
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. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html